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335,846
Predicting the Probability of Correct Classification
"... We propose a formulation for binary classification, called the Probabilistic CDF algorithm, that both makes a classification prediction, and estimates the probability that the classification is correct. Our model space consists of the widely used basis function models (which includes support vec ..."
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We propose a formulation for binary classification, called the Probabilistic CDF algorithm, that both makes a classification prediction, and estimates the probability that the classification is correct. Our model space consists of the widely used basis function models (which includes support
Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data
, 2000
"... Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data ..."
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Cited by 569 (1 self)
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, and other normal tissues. The dataset consists of expression experiment results for 97 802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification
Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm
 Machine Learning
, 1988
"... learning Boolean functions, linearthreshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each ex ..."
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Cited by 773 (5 self)
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example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning
Boosting the margin: A new explanation for the effectiveness of voting methods
 IN PROCEEDINGS INTERNATIONAL CONFERENCE ON MACHINE LEARNING
, 1997
"... One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this ..."
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Cited by 897 (52 self)
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that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 561 (20 self)
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is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with errorcorrecting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound
Robust face recognition via sparse representation
 IEEE TRANS. PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2008
"... We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signa ..."
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Cited by 936 (40 self)
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signal representation offers the key to addressing this problem. Based on a sparse representation computed by ℓ 1minimization, we propose a general classification algorithm for (imagebased) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
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Cited by 440 (17 self)
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that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level
Ngrambased text categorization
 In Proc. of SDAIR94, 3rd Annual Symposium on Document Analysis and Information Retrieval
, 1994
"... Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in ..."
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Cited by 445 (0 self)
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is small, fast and robust. This system worked very well for language classification, achieving in one test a 99.8 % correct classification rate on Usenet newsgroup articles written in different languages. The system also worked reasonably well for classifying articles from a number of different computer
Shape matching and object recognition using low distortion correspondence
 In CVPR
, 2005
"... We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of correspond ..."
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Cited by 419 (15 self)
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datasets. One is the Caltech 101 dataset (FeiFei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48 % correct classification rate, compared to FeiFei et al’s 16%. We also show results for localizing frontal and profile faces that are comparable
Printed in Cheat Britain On the expected probability of correct classification
"... The Bayesian approach is applied to examine how the number of features used in a classification problem affects the performance. Exact and asymptotic results are derived for two particular classification problems, demonstrating the positive effect of increasing the number of features. ..."
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The Bayesian approach is applied to examine how the number of features used in a classification problem affects the performance. Exact and asymptotic results are derived for two particular classification problems, demonstrating the positive effect of increasing the number of features.
Results 1  10
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335,846