Results 1 - 10 of 100,282

Wrappers for Feature Subset Selection

by Ron Kohavi, George H. John - 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 1569 (3 self) - Add to MetaCart
, 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

Regularization and variable selection via the Elastic Net.

by Hui Zou , Trevor Hastie - J. R. Stat. Soc. Ser. B , 2005
"... Abstract We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, wher ..."
Abstract - Cited by 973 (11 self) - Add to MetaCart
Abstract We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect

Adaptive floating search methods in feature selection

by P. Somol , P. Pudil , J. Novovicova , P. Paclik - PATTERN RECOGNITION LETTERS , 1999
"... A new suboptimal search strategy for feature selection is presented. It represents a more sophisticated version of "classical" floating search algorithms (Pudil et al., 1994), attempts to remove some of their potential deficiencies and facilitates finding a solution even closer to the opti ..."
Abstract - Cited by 548 (21 self) - Add to MetaCart
A new suboptimal search strategy for feature selection is presented. It represents a more sophisticated version of "classical" floating search algorithms (Pudil et al., 1994), attempts to remove some of their potential deficiencies and facilitates finding a solution even closer

A Comparative Study on Feature Selection in Text Categorization

by Yiming Yang, Jan O. Pedersen , 1997
"... This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI), ..."
Abstract - Cited by 1320 (15 self) - Add to MetaCart
This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI

The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings

by Frank L. Schmidt, John E. Hunter - Psychological Bulletin , 1998
"... This article summarizes the practical and theoretical implications of 85 years of research in personnel selection. On the basis of meta-analytic findings, this article presents the validity of 19 selection procedures for predicting job performance and training performance and the validity of paired ..."
Abstract - Cited by 392 (2 self) - Add to MetaCart
This article summarizes the practical and theoretical implications of 85 years of research in personnel selection. On the basis of meta-analytic findings, this article presents the validity of 19 selection procedures for predicting job performance and training performance and the validity of paired

Verb Semantics And Lexical Selection

by Zhibiao Wu , 1994
"... ... structure. As Levin has addressed (Levin 1985), the decomposition of verbs is proposed for the purposes of accounting for systematic semantic-syntactic correspondences. This results in a series of problems for MT systems: inflexible verb sense definitions; difficulty in handling metaphor and new ..."
Abstract - Cited by 551 (4 self) - Add to MetaCart
and new usages; imprecise lexical selection and insufficient system coverage. It seems one approach is to apply probability methods and statistical models for some of these problems. However, the question reminds: has PSR exhausted the potential of the knowledge-based approach? If not, are there any

Irrelevant Features and the Subset Selection Problem

by George H. John, Ron Kohavi, Karl Pfleger - MACHINE LEARNING: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL , 1994
"... 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 that the definitions used in the machine learning literature do not adequately partition the features ..."
Abstract - Cited by 757 (26 self) - Add to MetaCart
not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets.

Regression Shrinkage and Selection Via the Lasso

by Robert Tibshirani - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
Abstract - Cited by 4212 (49 self) - Add to MetaCart
We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients

Feature detection with automatic scale selection

by Tony Lindeberg - International Journal of Computer Vision , 1998
"... The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works ..."
Abstract - Cited by 723 (34 self) - Add to MetaCart
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal

Selection of relevant features and examples in machine learning

by Avrim L. Blum, Pat Langley - ARTIFICIAL INTELLIGENCE , 1997
"... 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 features, and the problem of selecting relevant examples. We describe the advances that have been mad ..."
Abstract - Cited by 606 (2 self) - Add to MetaCart
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 features, and the problem of selecting relevant examples. We describe the advances that have been
Results 1 - 10 of 100,282