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831,101
Irrelevant Features and the Subset Selection Problem
 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 highaccuracy 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 ..."
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Cited by 741 (26 self)
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We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small highaccuracy 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
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 ..."
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Cited by 1522 (3 self)
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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
Regression Shrinkage and Selection Via the Lasso
 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 ..."
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Cited by 4055 (51 self)
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that are exactly zero and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also
Least angle regression
 Ann. Statist
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
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Cited by 1308 (43 self)
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The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope
Heuristic Evaluation of User Interfaces
 IN: PROCEEDINGS OF THE CHI´90 CONFERENCE, SEATTLE
, 1990
"... Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four experiments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found betw ..."
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Cited by 502 (4 self)
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Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four experiments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found
Projection Pursuit Regression
 Journal of the American Statistical Association
, 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
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Cited by 555 (6 self)
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A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures
The FF planning system: Fast plan generation through heuristic search
 Journal of Artificial Intelligence Research
, 2001
"... We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be ind ..."
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Cited by 822 (53 self)
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We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts
Quantile Regression
 JOURNAL OF ECONOMIC PERSPECTIVES—VOLUME 15, NUMBER 4—FALL 2001—PAGES 143–156
, 2001
"... We say that a student scores at the fifth quantile of a standardized exam if he performs better than the proportion � of the reference group of students and worse than the proportion (1–�). Thus, half of students perform better than the median student and half perform worse. Similarly, the quartiles ..."
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Cited by 937 (10 self)
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We say that a student scores at the fifth quantile of a standardized exam if he performs better than the proportion � of the reference group of students and worse than the proportion (1–�). Thus, half of students perform better than the median student and half perform worse. Similarly
Regression quantiles
 Econometrica
, 1978
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 870 (19 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Results 1  10
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