Results 1  10
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369,294
Bayesian Network Classifiers
, 1997
"... 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 stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
Abstract

Cited by 793 (20 self)
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represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally
Wrappers for Feature Subset Selection
 AIJ SPECIAL ISSUE ON RELEVANCE
, 1997
"... 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 achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
Abstract

Cited by 1567 (3 self)
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, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study
Choosing multiple parameters for support vector machines
 MACHINE LEARNING
, 2002
"... The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing para ..."
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Cited by 472 (17 self)
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parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.
Correlationbased feature selection for machine learning
, 1998
"... A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that ..."
Abstract

Cited by 319 (3 self)
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this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments
Feature Subset Selection Using A Genetic Algorithm
, 1997
"... : Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) ..."
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Cited by 280 (7 self)
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of randomized heuristic search techniques, offer an attractive approach to find nearoptimal solutions to such optimization problems. This paper presents an approach to feature subset selection using a genetic algorithm. Some advantages of this approach include the ability to accommodate multiple criteria
Induction of Selective Bayesian Classifiers
 CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1994
"... In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space ..."
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Cited by 265 (7 self)
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In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space
Coalition Structure Generation with Worst Case Guarantees
, 1999
"... Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition ..."
Abstract

Cited by 270 (9 self)
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Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition
An Implicit Enumeration Algorithm to Generate Tests for Combinational Logic Circuits
 IEEE Transactions on Computers
, 1981
"... The DAlgorithm (DALG) is shown to be ineffective for the class of combinational logic circuits that is used to implement Error Correction and Translation (ECAT) functions. PODEM (PathOriented Decision Making) is a new test generation algorithm for combinational logic circuits. PODEM uses an implic ..."
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Cited by 258 (0 self)
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simplicity when compared to the DAlgorithm. PODEM is a complete algorithm in that it will generate a test if one exists. Heuristics are used to achieve an efficient implicit search of the space of all possible primary input patterns until either a test is found or the space is exhausted.
Mondrian multidimensional kanonymity
 in Proc. 22nd ICDE. IEEE
"... KAnonymity has been proposed as a mechanism for privacy protection in microdata publishing, and numerous recoding “models ” have been considered for achieving kanonymity. This paper proposes a new multidimensional model, which provides an additional degree of flexibility not seen in previous (sing ..."
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Cited by 255 (5 self)
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anonymity models). However, we introduce a simple, scalable, greedy algorithm that produces anonymizations that are a constantfactor approximation of optimal. Experimental results show that this greedy algorithm frequently leads to more desirable anonymizations than two optimal exhaustivesearch algorithms
by Exhaustive Search for Functional Symmetries
"... Separate optimizations of logic and layout have been thoroughly studied in the past and are well documented for common benchmarks. However, to be competitive, modern circuit optimizations must use physical and logic information simultaneously. In this work, we propose new algorithms for rewiring and ..."
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or after detailed placement, it approximately doubles the improvement in wirelength. Our contributions are based on exhaustive search for functional symmetries in subcircuits consisting of several gates. Our graphbased symmetry finding is more comprehensive than previously known algorithms — it detects
Results 1  10
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