This directory is created automatically and some papers may be mislabeled. Only document within the CiteSeer database are listed. The directory is intended to provide entry points for browsing the database and is not intended to be authoritative. Papers may not appear in all relevant categories. For example, papers in a sub-category may not appear in higher level categories.
488 Fast Algorithms for Mining Association Rules - Agrawal, Srikant (1994)(Correct)
We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally differen... / The closest work in the machine learning literature is the KID br Fast Algorithms for Mining Association Rules Rakesh Agrawal
426 Bagging Predictors - Breiman (1996)(Correct)
Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical ou... / ftp ics.uci.edu pub machine-learning-databases The data are
373 Complements to 'Pattern Recognition and Neural Networks' - Ripley (1996)(Correct)
Introduction Page 4: The book by Przytula & Prasanna (1993) discusses in detail the parallel implementation of neural networks. Page 16: Langley (1996) provides a book-length introduction to one viewp... / to one viewpointon machine learning. Langley Simon and
291 Experiments with a New Boosting Algorithm - Freund, Schapire (1996)(Correct)
In an earlier paper, we introduced a new "boosting"
algorithm called AdaBoost which, theoretically, can be used to
significantly reduce the error of any learning algorithm that consistently
generate... / Machine Learning Proceedings of the
236 Reinforcement Learning: A Survey - Leslie Pack Kaelbling, Michael L.. (1996)(Correct)
This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both
the historical basis of t... / to researchers familiar with machine learning. Both the historical basis br issues in temporal difference learning. Machine Learning - .
212 LEDA - A Platform for Combinatorial and Geometric Computing - Mehlhorn, Näher (1995)(Correct)
LEDA is a library of efficient data types and algorithms in combinatorial and
geometric computing. The main features of the library are its wide collection
of data types and algorithms, the precise an... / planning traffic scheduling machine learning and computational biology.
210 Irrelevant Features and the Subset Selection Problem - John, Kohavi, Pfleger (1994)(Correct)
We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show th... / W. Cohen Haym Hirsh eds.Machine Learning Proceedings of the Eleventh br discovery in empirical learning. Machine Learning - .
200 A Tutorial on Support Vector Machines for Pattern Recognition - Burges (1998)(Correct)
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, wo... / International Conference on Machine Learning pages - Bari Italy br of a Pattern Recognition Learning Machine There is a remarkable
185 Mining Generalized Association Rules - Srikant, Agrawal (1995)(Correct)
We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the item... / Mining Generalized Association Rules Ramakrishnan Srikant
183 Fast Effective Rule Induction - Cohen (1995)(Correct)
Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collectio... / To appear in Machine Learning Proceedings of the Twelfth br discovery in empirical learning. Machine Learning .
179 Learning to Act using Real-Time Dynamic Programming - Barto, Bradtke, Singh (1995)(Correct)
Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning ... / engineering. Similarly machine learning techniques suited to embedded br issues in temporal difference learning. Machine Learning -
178 The CN2 Induction Algorithm - Clark, Niblett (1989)(Correct)
Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evalua... / In Machine Learning Journal pp -
155 Neural Network-Based Face Detection - Rowley, Baluja, Kanade (1996)(Correct)
We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The s... / Artificial neural networks Machine learning Introduction In this
127 Boosting a Weak Learning Algorithm By Majority - Freund (1995)(Correct)
We present an algorithm for improving the accuracy of algorithms for learning
binary concepts. The improvement is achieved by combining a large number of hypotheses,
each of which is generated by trai... / In many actual machine learning scenarios the training set
123 Goal-directed Requirements Acquisition - Dardenne, van Lamsweerde, Fickas (1993)(Correct)
Requirements analysis includes a preliminary acquisition step where a global model for the specification of the
system and its environment is elaborated. This model, called requirements model, involve... / and the application of machine learning technology Vla a Two br Vla a A. van Lamsweerde Learning Machine Learning in Introducing a
120 WebWatcher: A Tour Guide for the World Wide Web - Joachims, Freitag, Mitchell (1997)(Correct)
We explore the notion of a tour guide software agent for assisting users browsing the World Wide Web. A Web tour guide agent provides assistance similar to that provided by a human tour guide in a mus... / can learn that a term such as machine learning matches a hyperlink such as
117 Sampling Large Databases for Association Rules - Toivonen (1996)(Correct)
Discovery of association rules is an important database mining problem. Current algorithms for finding association rules require several passes over the analyzed database, and obviously the role of I/... / Workshop on Statistics Machine Learning and Knowledge Discovery in br Sampling Large Databases for Association Rules Hannu Toivonen University
117 Discovery of Multiple-Level Association Rules from Large Databases - Han (1995)(Correct)
Previous studies on mining association rules find rules at single concept level, however, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete kn... / S. Michalski and G. Tecuci. Machine Learning A Multistrategy Approach br Discovery of Multiple-Level Association Rules from Large Databases
114 Solving Multiclass Learning Problems via Error-Correcting Output Codes - Dietterich, al. (1995)(Correct)
Multiclass learning problems involve finding a definition for an unknown function f(x)
whose range is a discrete set containing k ? 2 values (i.e., k "classes"). The definition is
acquired by studyin... / be traced to early research in machine learning Duda Machanik br CA. Nilsson N. J. Learning Machines. McGraw-Hill New York.
104 Markov games as a framework for multi-agent reinforcement learning - Littman (1994)(Correct)
In the Markov decision process (MDP) formalization
of reinforcement learning, a single adaptive
agent interacts with an environment defined by a
probabilistic transition function. In this solipsistic
... / In Proceedings of the Machine Learning Conference. To appear. br C. H. and Dayan P. . Q-learning. Machine Learning - .
103 Wrappers for Feature Subset Selection - Kohavi, John (1997)(Correct)
In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achiev... / problem. In supervised machine learning an induction algorithm is
96 Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents - Tan (1993)(Correct)
Intelligent human agents exist in a cooperative
social environment that facilitates
learning. They learn not only by trialand
-error, but also through cooperation by
sharing instantaneous information,... / and a model for multi-agent machine learning. In Y. Kodratoff Ed. br P. Technical Note Q-Learning. Machine Learning Kluwer
93 Efficient Algorithms for Discovering Association Rules - Mannila, Toivonen, Verkamo (1994)(Correct)
Association rules are statements of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B". Agrawal, Imielinski, and Swami int... / artificial intelligence and machine learning see e.g. The area br Algorithms for Discovering Association Rules Heikki Mannila Hannu
93 Beyond Market Baskets: Generalizing Association Rules to Correlations - Brin, Motwani, Silverstein (1997)(Correct)
One of the most well-studied problems in data mining is mining
for association rules in market basket data. Association
rules, whose significance is measured via support and confidence,
are intended t... / Market Baskets Generalizing Association Rules to Correlations Sergey br case of finding association rules. Association rules whose significance is
91 Greedy Attribute Selection - Caruana, Freitag (1994)(Correct)
Many real-world domains bless us with a wealth
of attributes to use for learning. This blessing is
often a curse: most inductive methods generalize
worse given too many attributes than if given a
goo... / tasks. INTRODUCTION As machine learning is applied to real-world
90 Collaborative Interface Agents - Lashkari, Metral, Maes (1994)(Correct)
Interface agents are semi-intelligent systems which assist users with daily computer-based tasks. Recently, various researchers have proposed a learning approach towards building such agents and some ... / computer programs that employ machine learning techniques in order to
88 A System for Induction of Oblique Decision Trees - Murthy, Kasif, Salzberg (1994)(Correct)
This article describes a new system for induction of oblique decision trees. This system,
OC1, combines deterministic hill-climbing with two forms of randomization to find a good
oblique split (in the... / and opportunity for automated machine learning techniques. The advent of br Nilsson N. Learning Machines. Morgan Kaufmann San Mateo
87 An Effective Hash-Based Algorithm for Mining Association Rules - Park, Yu (1995)(Correct)
In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering lar... / Induction of Decision Trees. Machine Learning - . br Algorithm for Mining Association Rules Jong Soo Park
83 The Utility of Knowledge in Inductive Learning - Pazzani, Kibler (1992)(Correct)
In this paper, we demonstrate how different forms of background knowledge can
be integrated with an inductive method for generating constant-free Horn clause
rules. Furthermore, we evaluate, both theo... / International Conference on Machine Learning pp. - Ann Arbor br methods for inductive learning. Machine Learning - .
80 Data Mining: An Overview from a Database Perspective - Chen, Han, Yu (1996)(Correct)
Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an i... / topic in database systems and machine learning and by many industrial br mining knowledge discovery association rules classification data
78 SLIQ: A Fast Scalable Classifier for Data Mining - Mehta, Agrawal, Rissanen (1996)(Correct)
Classification is an important problem in the emerging field
of data mining. Although classification has been studied extensively in
the past, most of the classification algorithms are designed only... / largest dataset in the Irvine Machine Learning repositary is only KB
78 An Evaluation of Statistical Approaches to Text Categorization - Yang (1998)(Correct)
This paper focuses on a comparative evaluation of a wide-range of text categorization
methods, including previously published results on the Reuters corpus
and new results of additional experiments... / Tree DTree is a well-known machine learning approach to automatic
77 Mining Association Rules with Item Constraints - Srikant, Vu, Agrawal(Correct)
The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often int... / Mining Association Rules with Item Constraints
77 The Schema Theorem and Price's Theorem - Altenberg (1995)(Correct)
Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting ar... / in Search Optimization and Machine Learning. Addison Wesley.
75 Learning in the Presence of Malicious Errors - Kearns (1993)(Correct)
In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of ma... / motivation for the model of machine learning we study. This model was
74 Removing the Genetics from the Standard Genetic Algorithm - Baluja, Caruana (1995)(Correct)
We present an abstraction of the genetic algorithm (GA), termed population-based incremental learning
(PBIL), that explicitly maintains the statistics contained in a GA's population, but which abstrac... / International Conference on Machine Learning Lake Tahoe CA. July
71 Hierarchically classifying documents using very few words - Koller, Sahami (1997)(Correct)
The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which igno... / suited to the application of machine learning techniques. We have a
68 Exploratory Mining and Pruning Optimizations of Constrained.. - Ng (1998)(Correct)
From the standpoint of supporting human-centered discovery of knowledge, the present-day model of mining association rules suffers from the following serious shortcomings: (i) lack of user exploration... / present-day model of mining association rules suffers from the following
68 Error Reduction through Learning Multiple Descriptions - Ali, Pazzani (1996)(Correct)
Learning multiple descriptions for each class in the data has been shown to reduce
generalization error but the amount of error reduction varies greatly from domain to domain.
This paper presents a ... / more irrelevant attributes. Machine Learning VolNum - Year c
66 Error-Correcting Output Coding Corrects Bias and Variance - Kong, Dietterich (1995)(Correct)
Previous research has shown that a technique
called error-correcting output coding
(ECOC) can dramatically improve the
classification accuracy of supervised learning
algorithms that learn to classify ... / English text to speech A machine learning approach. Tech. rep. br Nilsson N. J. Learning Machines. McGrawHill New York.
65 Dynamic Parameter Encoding for Genetic Algorithms - Schraudolph, Belew (1992)(Correct)
The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficien... / revised for publication in Machine Learning July Abstract
65 Combining Labeled and Unlabeled Data with Co-Training - Blum, Mitchell (1998)(Correct)
We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in w... / INTRODUCTION In many machine learning settings unlabeled examples
64 Toward Optimal Feature Selection - Koller, Sahami (1996)(Correct)
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computationally intractable, method for ... / feature subset selection in machine learning. As defined by John
62 Residual Algorithms: Reinforcement Learning with Function.. - Leemon Baird (1995)(Correct)
A number of reinforcement learning algorithms have
been developed that are guaranteed to converge to the
optimal solution when used with lookup tables. It is
shown, however, that these algorithms can ... / of temporal differences. Machine Learning - . Tesauro G. br in temporal difference learning. Machine Learning .
62 Transfer of Learning by Composing Solutions of Elemental Sequential.. - Singh (1992)(Correct)
Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on singl... / by the agent. In the machine learning literature closed loop br C. H. Dayan P. Q-learning. Machine Learning. to appear.
61 Bounds on the Sample Complexity of Bayesian Learning Using.. - Haussler (1994)(Correct)
In this paper we study a Bayesian or average-case model of concept learning with
a twofold goal: to provide more precise characterizations of learning curve (sample
complexity) behavior that depend on... / from the frequent claims of machine learning practitioners that sample
61 Neural Network Exploration Using Optimal Experiment Design - Cohn (1994)(Correct)
We consider the question "How should one act when the only goal is to learn as much as possible?" Building
on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Opti... / its uncertainties. Most machine learning research however treats
60 Rule Induction with CN2: Some Recent Improvements - Clark, Boswell (1991)(Correct)
The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we ... / In Machine Learning -Proceedings of the Fifth
59 Knowledge-Based Artificial Neural Networks - Towell, Shavlik (1994)(Correct)
Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples
to develop a method for accurately classifying examples not seen during training. The challenge of
hybrid... / Madison WI Keywords machine learning connectionism br Methods for Inductive Learning Machine Learning
56 Learning Decision Trees using the Fourier Spectrum - Kushilevitz, Mansour (1991)(Correct)
This work gives a polynomial time algorithm for learning decision trees with respect
to the uniform distribution. (This algorithm uses membership queries.) The decision tree
model that is considered i... / a theoretical basis for machine learning. These efforts involved
56 Discovery of frequent episodes in event sequences - Mannila, Toivonen, Verkamo (1997)(Correct)
Sequences of events describing the behavior and actions of users or systems can be collected in several domains. We consider the problem of discovering frequently occurring episodes in such sequences.... / Most data mining and machine learning techniques are adapted br also basically the same for association rules and Winepi. The levelwise
55 Reinforcement Learning with Replacing Eligibility Traces - Singh (1996)(Correct)
The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze i... / Machine Learning - c fl br issues in temporal difference learning. Machine Learning
55 Scalable Parallel Data Mining for Association Rules - Han, Karypis, Kumar (1997)(Correct)
In this paper we propose two new parallel formulations
of the Apriori algorithm that is used for computing
association rules. These new formulations, IDD and HD, address
the shortcomings of two previo... / Parallel Data Mining for Association Rules Eui-Hong Sam Han
54 Active Learning with Statistical Models - Cohn, Ghahramani, Jordan (1996)(Correct)
For many types of machine learning algorithms, one can compute the statistically "optimal
" way to select training data. In this paper, we review how optimal data selection
techniques have been used w... / Abstract For many types of machine learning algorithms one can compute br Queries and concept learning. Machine Learning - .
54 Empirical Support for Winnow and Weighted-Majority Algorithms.. - Blum (1995)(Correct)
This paper describes experimental results on using Winnow and Weighted-Majority based algorithms on a real-world calendar scheduling domain. These two algorithms have been highly studied in the theore... / studied in the theoretical machine learning literature. We show here that
53 A Unifying Review of Linear Gaussian Models - Sam Roweis (1997)(Correct)
Factor analysis, principal component analysis (PCA), mixtures of Gaussian clusters, vector
quantization (VQ), Kalman filter models and hidden Markov models can all be unified as
variations of unsuperv... / network form more common in machine learning. Notice that there is
49 Improving Generalization with Active Learning - Cohn, Atlas, al. (1992)(Correct)
Active learning differs from passive "learning from examples" in that the learning algorithm assumes
at least some control over what part of the input domain it receives information about. In some sit... / If As published in Machine Learning - . A
49 A New SQL-like Operator for Mining Association Rules - Meo, Psaila, al. (1996)(Correct)
Data mining evolved as a collection of applicative
problems and efficient solution algorithms
relative to rather peculiar problems, all focused
on the discovery of relevant information hidden
in datab... / from Statistics Neural Nets Machine Learning and Expert Systems. br SQL-like Operator for Mining Association Rules Rosa Meo Dipartimento
49 Relational Instance-Based Learning - Emde, Wettschereck (1996)(Correct)
A relational instance-based learning algorithm, called Ribl, is motivated and developed in this paper. We argue that instancebased methods offer solutions to the often unsatisfactory behavior of curre... / International Conference on Machine Learning L. Saitta ed.Morgan
48 The RoboCup Synthetic Agent Challenge 97 - Kitano, Tambe, Stone (1997)(Correct)
RoboCup Challenge offers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multiagent domain. While RoboCup in general envisions longer range ... / a novel opportunity for machine learning planning and multi-agent
47 Competitive Environments Evolve Better Solutions for Complex Tasks - Angeline, Pollack (1993)(Correct)
In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails... / is a long standing topic in machine learning Samuel Tesauro br in temporal difference learning Machine Learning - .
47 A Simple Weight Decay Can Improve Generalization - Krogh (1992)(Correct)
It has been observed in numerical simulations that a weight decay can improve
generalization in a feed-forward neural network. This paper explains
why. It is proven that a weight decay has two effects... / neural network or any other learning machine'depends on a balance
47 Stable Function Approximation in Dynamic Programming - Gordon (1995)(Correct)
The success of reinforcement learning in practical problems depends on the ability to combine function approximation with temporal difference methods such as value iteration. Experiments in this area ... / -is an integral part of machine learning. The methods of temporal br approximation and Q-learning. Machine Learning -
46 Selection of Relevant Features and Examples in Machine Learning - Blum, Langley (1997)(Correct)
In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant f... / Features and Examples in Machine Learning Avrim L. Blum br discovery in empirical learning. Machine Learning - .
46 Efficient Algorithms for Minimizing Cross Validation Error - Moore, Lee (1994)(Correct)
Model selection is important in many areas of
supervised learning. Given a dataset and a set
of models for predicting with that dataset, we
must choose the model which is expected to best
predict futu... / Be Applicable Elsewhere In Machine Learning. Racing The Cross
45 Incremental Multi-Step Q-Learning - Peng, Williams (1996)(Correct)
This paper presents a novel incremental algorithm that combines Q-learning, a
well-known dynamic programming-based reinforcement learning method, with the TD() return
estimation process, which is ty... / less data and less time. Machine Learning - . Pendrith br C. H. Dayan P. Q-learning. Machine Learning - .
45 Levelwise search and borders of theories in knowledge discovery - Mannila, Toivonen (1997)(Correct)
One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all senten... / it seems that techniques from machine learning statistics and databases br Mining Borders of Theories Association Rules Episodes Integrity
44 Learning to Fly - Sammut (1992)(Correct)
This paper describes experiments in applying inductive
learning to the task of acquiring a complex
motor skill by observing human subjects. A
flight simulation program has been modified to
log the act... / experiments that demonstrate machine learning of a reactive strategy to
44 Flexible Metric Nearest Neighbor Classification - Friedman (1994)(Correct)
The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually defined in terms of a... / popular in statistics and machine learning. Introduction
43 Overcoming Incomplete Perception with Utile Distinction Memory - McCallum (1993)(Correct)
This paper presents a method by which a
reinforcement learning agent can solve the
incomplete perception problem using memory.
The agent uses a hidden Markov model
(HMM) to represent its internal stat... / International Conference on Machine Learning Austin Texas . Morgan
42 Selection of Relevant Features in Machine Learning - Langley (1994)(Correct)
In this paper, we review the problem of selecting relevant
features for use in machine learning. We describe
this problem in terms of heuristic search through a
space of feature sets, and we identify ... / of Relevant Features in Machine Learning Pat Langley
41 Data Mining - The Search for Knowledge in Databases - Holsheimer, Siebes (1991)(Correct)
Data mining is the search for relationships and global patterns that exist in large databases, but are `hidden'
among the vast amounts of data, such as a relationship between patient data and their me... / as taken from the area of machine learning. Another important problem br and databases. . Machine learning Machine learning has a long
41 Tracking the Best Expert - Mark Herbster (1995)(Correct)
We generalize the recent worst-case loss bounds
for on-line algorithms where the additional loss
of the algorithm on the whole sequence of examples
over the loss of the best expert is bounded.
The gen... / linear-threshold algorithm. Machine Learning - . Lit
41 Adaptive Web Sites: an AI Challenge - Perkowitz (1997)(Correct)
The creation of a complex web site is a thorny problem in user interface design. First, different visitors have distinct goals. Second, even a single visitor may have different needs at different time... / projects in plan recognition machine learning knowledge representation
41 Theory and Applications of Agnostic PAC-learning with Small Decision.. - Auer (1995)(Correct)
We exhibit a new algorithm T2 for agnostic PAC-learning with decision trees of
at most 2-levels, whose computation time is almost linear in the size of the training
set. We evaluate the performance of... / that are considered in applied machine learning for which no guarantee
40 Cost-Sensitive Classification: Empirical Evaluation of a Hybrid.. - Turney (1995)(Correct)
This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET
uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm.
The fitness funct... / areas. There are several machine learning algorithms that consider the br algorithms for concept learning. Machine Learning - .
39 Tight Performance Bounds on Greedy Policies Based on Imperfect Value.. - Williams (1993)(Correct)
Consider a given value function on states of a Markov decision problem, as might result from applying a reinforcement learning algorithm. Unless this value function equals the corresponding optimal va... / optimalvalue functions. Machine Learning. Sutton R. S. br C. H. Dayan P. Q-learning. Machine Learning - .
39 Using experience in learning and problem solving - Koton (1989)(Correct)
This paper contains a brief overview of case-based reasoning (CBR) with an emphasis on
European activities in the field. The main objective was to have a balance between brevity
and expressiveness and... / in automated reasoning and machine learning. In case-based reasoning a
38 Knowledge Discovery and Data Mining: Towards a Unifying Framework - Fayyad, Piatetsky-Shapiro, Smyth (1996)(Correct)
This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe links
between data mining, knowledge discovery, and other related fields. We then define... / artificial intelligence and machine learning. In our view KDD refers to br the derivation of summary or association rules and the use of multivariate
38 Niching Methods for Genetic Algorithms - Mahfoud (1995)(Correct)
Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function opti... / include classification and machine learning multimodal function
36 Integrating Classification and Association Rule Mining - Liu (1998)(Correct)
Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy so... / existing algorithms in the machine learning literature that can be used br Integrating Classification and Association Rule Mining Bing Liu Wynne Hsu
36 PAC-Learnability of Determinate Logic Programs - Dzeroski, Muggleton, Russell (1992)(Correct)
The field of Inductive Logic Programming
(ILP) is concerned with inducing logic programs
from examples in the presence of background
knowledge. This paper defines the ILP
problem, and describes the v... / successes within the field of machine learning have derived from systems
36 Addressing the Selective Superiority Problem: Automatic.. - Brodley (1993)(Correct)
The results of empirical comparisons of existing
learning algorithms illustrate that each
algorithm has a selective superiority; it is best
for some but not all tasks. Given a data set,
it is often no... / A recent focus of research in machine learning is to understand the tasks br Nilsson N. J. Learning machines. New York McGraw-Hill.
36 Learning Simple Concepts Under Simple Distributions - Li (1991)(Correct)
This is a preliminary draft version. The journal version [SIAM. J. Computing, 20:5(1991), 911935 ] is the correct final version. However, the polynomial time computable universal distribution section ... / at odds with the notion that machine learning should be practically useful.
35 An Adaptive Web Page Recommendation Service - Balabanovic (1997)(Correct)
An adaptive recommendation service seeks to adapt
to its users, providing increasingly personalized recommendations
over time. In this paper we introduce
the "Fab" adaptive web page recommendation ser... / lies at the intersection of machine learning ML and IR and there is a
35 Forming Concepts for Fast Inference - Kautz, Selman (1992)(Correct)
Knowledge compilation speeds inference by creating tractable approximations of a knowledge
base, but this advantage is lost if the approximations are too large. We show how learning
concept generaliza... / Introduction Work in machine learning has traditionally been br discovery in empirical learning. Machine Learning - .
34 Induction of First-Order Decision Lists: Results on Learning the Past .. - Mooney (1995)(Correct)
This paper presents a method for inducing logic programs from examples that learns
a new class of concepts called first-order decision lists, defined as ordered lists of clauses
each ending in a cut. ... / ILP is a growing subtopic of machine learning that studies the induction br knowledge in inductive learning. Machine Learning - .
34 Data Mining using MLC++ - A Machine Learning Library in C++ - Kohavi, Sommerfield, Dougherty (1997)(Correct)
Data mining algorithms including machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespre... / Data Mining using MLCA Machine Learning Library in C br genetic algorithms and association rules. NeoVista focuses heavily
33 Building Classifiers using Bayesian Networks - Friedman (1996)(Correct)
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the... / in many applications of machine learning and there are numerous
33 Fast Sequential and Parallel Algorithms for Association Rule Mining.. - Mueller (1995)(Correct)
The field of knowledge discovery in databases, or "Data Mining", has received increasing attention during recent years as large organizations have begun to realize the potential value of the informati... / of computer science including machine learning expert systems and knowledge br and Parallel Algorithms for Association Rule Mining A Comparison
32 Surface Approximation and Geometric Partitions - Agarwal, Suri (1994)(Correct)
Motivated by applications in computer graphics, visualization, and scientific computation, we study the computational complexity of the following problem: Given a set S of n points sampled from a biva... / following problem arising in machine learning given n red' and m
32 Lamarckian Learning in Multi-agent Environments - Grefenstette (1991)(Correct)
Genetic algorithms gain much of their power from
mechanisms derived from the field of population
genetics. However, it is possible, and in some
cases desirable, to augment the standard mechanisms
with... / to explore the application of machine learning techniques to reactive
32 Map Learning and High-Speed Navigation in RHINO - Thrun, Bücken, Burgard, Fox.. (1998)(Correct)
This chapter surveys basic methods for learning maps and high speed autonomous navigation for indoor
mobile robots. The methods have been developed in our lab over the past few years, and most of them... / robot RHINO. ffl Learning. Machine learning algorithms are employed to br The robot RHINO. ffl Learning. Machine learning algorithms are
32 Integrated Support For Data Archaeology - Brachman (1993)(Correct)
Corporate databases increasingly are being viewed as potentially rich sources
of new and valuable knowledge. Various approaches to"discovering" or "mining
" such knowledge have been proposed. Here we ... / automatic statistical or machine-learning mechanisms to search for
32 A Perspective on Databases and Data Mining - Holsheimer, Kersten, Mannila.. (1995)(Correct)
We discuss the use of database methods for data mining. Recently impressive results have been achieved for some data mining problems using highly specialized and clever data structures. We study how w... / an area in the intersection of machine learning statistics and databases. br area the discovery of association rules. We present a simple
32 Rigorous Learning Curve Bounds from Statistical Mechanics - Haussler (1996)(Correct)
In this paper we introduce and investigate a mathematically
rigorous theory of learning curves that is based on ideas
from statistical mechanics. The advantage of our theory over
the well-established ... / out and analyzed in other machine learning work Of course br the VC dimension of a learning machine. Neural Comput. . To
32 Learning at the Knowledge Level - Dietterich (1986)(Correct)
When Newell introduced the concept of the knowledge level as a useful level of description for computer
systems, he focused on the representation of knowledge. This paper applies the knowledge
level n... / arise. First some existing machine learning programs appear to be
31 Compiling Prior Knowledge Into an Explicit Bias - Cohen (1992)(Correct)
Current theory-guided learning systems are inflexible, in that they are committed to performing one particular class of theory corrections; this is problematic because in some cases special-purpose th... / International Conference on Machine Learning Compiling Prior Knowledge br methods for inductive learning. Machine Learning .
31 Boosting and Rocchio Applied to Text Filtering - Schapire, Singer, Singhal (1998)(Correct)
We discuss two learning algorithms for text filtering: modified
Rocchio and a boosting algorithm called AdaBoost. We show
how both algorithms can be adapted to maximize any general
utility matrix that... / two different communities -machine learning ML and information
31 Reusing Proofs - Kolbe, Walther (1994)(Correct)
1
We develop a learning component for a theorem
prover designed for verifying statements by mathematical induction.
If the prover has found a proof, it is analyzed yielding
a so-called catch. The c... / induction in the sense of machine learning. Induction proofs may be
30 Genetic Algorithms and Artificial Life - Mitchell, Forrest (1993)(Correct)
Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificia... / GAs have been used for many machine-learning applications including
30 Algorithms for Mining Distance-Based Outliers in Large Datasets - Knorr, Ng (1998)(Correct)
This paper deals with finding outliers (exceptions)
in large, multidimensional datasets.
The identification of outliers can lead to the
discovery of truly unexpected knowledge in areas
such as electro... / some existing algorithms in machine learning and data mining have br research in data mining e.g.association rules AIS MTV MT
30 DBMiner: A System for Mining Knowledge in Large Relational Databases - Han (1996)(Correct)
A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, incl... / R. . C . Programs for Machine Learning. Morgan Kaufmann. Shen br Evolution Evaluator Association Rule Finder Discovery Modules
29 Experiments on Multistrategy Learning by Meta-Learning - Chan (1993)(Correct)
In this paper, we propose meta-learning as a general technique
to combine the results of multiple learning algorithms,
each applied to a set of training data. We detail several metalearning
strategies... / encouraging results. Machine learning techniques are central to br mehtods for inductive learning. Machine Learning -