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.
1234.7 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
1204.9 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
1142.8 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
1081.1 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
843.4 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
709.0 An Evaluation of Statistical Approaches to Text Categorization - Yang (1999)(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. A... / Tree DTree is a well-known machine learning approach to automatic
684.0 Reinforcement Learning: A Survey - Kaelbling, Littman, Moore (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 -
614.4 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.
559.9 Additive Logistic Regression: a Statistical View of Boosting - Friedman (1998)(Correct)
Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be... / Boosting was proposed in the machine learning literature Freund Schapire
536.2 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
530.4 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 .
518.8 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 -
518.5 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 - .
510.6 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
449.2 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
438.2 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
395.7 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
388.5 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
371.4 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
368.1 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
339.1 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
339.1 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
330.4 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.
302.1 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
256.7 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 - .
253.6 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
252.1 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
238.2 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
234.2 Tracking the Best Expert - Herbster, Warmuth (1998)(Correct)
We generalize the recent relative 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... / Machine Learning NN - c fl
234.0 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
231.8 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
229.6 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
226.0 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
225.5 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
224.6 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
223.1 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.
222.2 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
217.2 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
214.4 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
205.7 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
204.2 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
197.9 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
197.1 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
195.7 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 - .
191.4 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
191.3 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.
190.9 A Hierarchical Approach to Wrapper Induction - Muslea, Minton, Knoblock (1999)(Correct)
With the tremendous amount of information that becomes available on the Web on a daily basis, the ability to quickly develop information agents has become a crucial problem. A vital component of any W... / this paper we introduce a new machine learning method for wrapper
185.5 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
182.8 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
181.8 Greedy Function Approximation: A Gradient Boosting Machine - Friedman (1999)(Correct)
Function approximation is viewed from the perspective of numerical optimization in
function space, rather than parameter space. A connection is made between stagewise additive
expansions and steepest-... / wavelet-like dictionary. In machine learning is called
179.7 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 .
178.0 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 -
177.1 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
174.4 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
172.7 A Survey of Methods for Scaling Up Inductive Algorithms - Provost, Kolluri (1999)(Correct)
One of the defining challenges for the KDD research community is to enable inductive
learning algorithms to mine very large databases. By collecting, categorizing, and summarizing
existing work on s... / backgrounds including machine learning statistics and databases br discovery in empirical learning. Machine Learning - .
171.4 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
159.4 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
156.5 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 - .
156.5 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
154.6 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
154.2 Integrating Association Rule Mining with Relational Database Systems: .. - Sarawagi (1998)(Correct)
Data mining on large data warehouses is becoming increasingly
important. In support of this trend, we consider a
spectrum of architectural alternatives for coupling mining
with database systems. These... / Integrating Association Rule Mining with Relational br rules classification rules association rules etc. The M-SQL
150.6 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
150.6 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
148.9 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
148.5 Data Mining Approaches for Intrusion Detection - Lee, Stolfo (1998)(Correct)
In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns o... / pattern recognition machine learning and database. Several types br that we have implemented the association rules algorithm and the frequent
148.5 Multi-class Support Vector Machines - Weston, Watkins (1998)(Correct)
this paper. Thanks
also to M. Stitson for writing the code for one-against-one and one-against-all SV
classification. We also thank Kai Vogtlaender for useful comments.
In communication with V. Vapnik... / problems from the UCI machine learning repository Where no br set of functions which the learning machine implements is chosen a
145.6 Knowledge-Based Artificial Neural Networks - Geoffrey Towell (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
145.4 Statistical Models for Text Segmentation - Beeferman, BERGER, LAFFERTY (1999)(Correct)
This paper introduces a new statistical approach to automatically partitioning text
into coherent segments. The approach is based on a technique that incrementally builds an
exponential model to ext... / may be cast as a problem in machine learning learn how to place breaks
144.6 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
143.1 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 - .
142.8 Nonlinear Component Analysis as a Kernel Eigenvalue Problem - Schölkopf, Smola, Müller (1998)(Correct)
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high--dim... / was known Burges the machine learning community has made little br and speed of support vector learning machines. In Advances in Neural
142.0 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
142.0 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
136.3 A Brief Introduction to Boosting - Schapire (1999)(Correct)
Boosting is a general method for improving the
accuracy of any given learning algorithm. This
short paper introduces the boosting algorithm
AdaBoost, and explains the underlying theory
of boosting, in... / framework for studying machine learning called the PAC learning
136.3 Information Extraction with HMMs and Shrinkage - Freitag, McCallum (1999)(Correct)
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling time series data, and have been applied with success to many language-related tasks such as part of speech tagging, speech re... / training data. As in many machine learning problems however the lack
136.2 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 -
131.4 Scalable Techniques for Mining Causal Structures - Silverstein, Brin, Motwani, Ullman (1998)(Correct)
Mining for association rules in market basket data has proved a fruitful area of research. Measures
such as conditional probability (confidence) and correlation have been used to infer rules
of the fo... / Networks from Data. Machine Learning pages - . br Abstract Mining for association rules in market basket data has
131.4 Optimization of Constrained Frequent Set Queries with 2-variable.. - Lakshmanan, Ng, Han, Pang (1998)(Correct)
Currently, there is tremendous interest in providing ad-hoc
mining capabilities in database management systems. As a
first step towards this goal, in [15] we proposed an architecture
for supporting co... / analysis program or a machine learning system should be that the br Since the introduction of association rules the development of
131.4 Ridge Regression Learning Algorithm in Dual Variables - Saunders, Gammerman, Vovk (1998)(Correct)
In this paper we study a dual version of the
Ridge Regression procedure. It allows us to
perform non-linear regression by constructing
a linear regression function in a high dimensional
feature space.... / ftp ftp.ics.uci.com pub machine-learning-databases housing. files br Our problem is to construct a learning machine which when given a new
130.4 Incremental Multi-Step Q-Learning - Peng, Williams (1996)(Correct)
This paper presents a novel incremental algorithm
that combines Q-learning, a wellknown
dynamic programming-based reinforcement
learning method, with the TD()
return estimation process, which is typic... / of TD for general Machine Learning - . Jaakkola T. br C. H. Dayan P. Q-learning. Machine Learning - .
125.7 Cyclic Association Rules - Özden, Ramaswamy, Silberschatz (1998)(Correct)
We study the problem of discovering association rules that display
regular cyclic variation over time. For example, if we compute
association rules over monthly sales data, we may observe seasonal
var... / Cyclic Association Rules Banu Ozden Sridhar
119.9 Active Markov Localization for Mobile Robots - Fox, Burgard, Thrun (1998)(Correct)
Localization is the problem of determining the position of a mobile robot from sensor data. Most existing
localization approaches are passive, i.e., they do not exploit the opportunity to control the ... / such as heuristic search and machine learning the value of active
119.9 Computing Iceberg Queries Efficiently - Min Fang (1998)(Correct)
Many applications compute aggregate functions
over an attribute (or set of attributes)
to find aggregate values above some specified
threshold. We call such queries iceberg
queries, because the numbe... / of producing interesting association rules PCY In this paper we
119.9 Support Vector Machine Reference Manual - Saunders, Stitson, Weston, Bottou.. (1998)(Correct)
this document will describe these programs. To find out more about SVMs, see the bibliography. We will not describe how SVMs work here. The first program we will describe is the paragen program, as it... / Machine SVM is a new type of learning machine. The SVM is a general
118.8 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
118.1 Active Learning for Natural Language Parsing and Information.. - Thompson, Califf, Mooney (1999)(Correct)
In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select f... / is an emerging area in machine learning that explores methods that br generalization with active learning. Machine Learning
115.9 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 - .
114.8 Improving the Accuracy and Speed of Support Vector Machines - Burges, Schölkopf (1997)(Correct)
Support Vector Learning Machines (SVM) are finding application
in pattern recognition, regression estimation, and operator inversion
for ill-posed problems. Against this very general backdrop,
any met... / International Conference on Machine Learning pp. - . br Abstract Support Vector Learning Machines SVM are finding
114.2 Robust classification systems for imprecise environments - Provost, Fawcett (1998)(Correct)
In real-world environments, it is usually difficult to
specify target operating conditions precisely. This uncertainty
makes building robust classification systems
problematic. We show that it is poss... / This strategy is common in machine learning pattern recognition data
113.5 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
112.0 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
110.1 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
110.1 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
109.0 Error-Correcting Output Coding for Text Classification - Berger (1999)(Correct)
This paper applies error-correcting output coding (ECOC) to the task of document categorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classification pro... / much recent interest in the machine learning community about algorithms
108.6 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
108.5 Maximum Entropy Models For Natural Language Ambiguity Resolution - Ratnaparkhi (1998)(Correct)
This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state-of-the-art accuracies using a single statistical modeling technique based on the principl... / . . The Machine Learning or Corpus-Based Approach . br is guided by American Bar Association rules or by state bar ethics
108.5 Temporal Sequence Learning and Data Reduction for Anomaly Detection - Lane (1998)(Correct)
The anomaly detection problem can be formulated as one of learning to characterize the behaviors of an individual, system, or network in terms of temporal sequences of discrete data. We present an app... / an authorized user. We take a machine learning viewpoint of this problem
108.5 Finding frequent substructures in chemical compounds - Dehaspe, Toivonen, King (1998)(Correct)
The discovery of the relationships between chemical structure and biological function is central to biological science and medicine. In this paper we apply data mining to the problem of predicting che... / contrasts with previous machine learning research on this problem br are beyond the complexity of association rules or their known variants.