Results 1 - 10 of 378

Using query-specific variance estimates to combine Bayesian classifiers

by Chi-hoon Lee, Russ Greiner, Shaojun Wang - In ICML ’06 , 2006
"... Many of today’s best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a novel “query specific ” combination rule: After learning a set of simple belief network classifiers, we produce an answer to each ..."
Abstract - Cited by 7 (3 self) - Add to MetaCart
Many of today’s best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a novel “query specific ” combination rule: After learning a set of simple belief network classifiers, we produce an answer

An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.

by Eric Bauer , Philip Chan , Salvatore Stolfo , David Wolpert - Machine Learning, , 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
Abstract - Cited by 707 (2 self) - Add to MetaCart
variants in conjunction with a decision tree inducer (three variants) and a Naive-Bayes inducer. The purpose of the study is to improve our understanding of why and when these algorithms, which use perturbation, reweighting, and combination techniques, affect classification error. We provide a bias

Combining Classifiers in Text Categorization

by Leah Larkey, W. Bruce Croft , 1996
"... Three different types of classifiers were investigated in the context of a text categorization problem in the medical domain: the automatic assignment of ICD9 codes to dictated inpatient discharge summaries. K-nearest-neighbor, relevance feedback, and Bayesian independence classifers were applied in ..."
Abstract - Cited by 163 (7 self) - Add to MetaCart
individually and in combination. A combination of different classifiers produced better results than any single type of classifier. For this specific medical categorization problem, new query formulation and weighting methods used in the k-nearest-neighbor classifier improved performance. 1 Introduction

On bias, variance, 0/1-loss, and the curse-of-dimensionality

by Jerome H. Friedman, Usama Fayyad - Data Mining and Knowledge Discovery , 1997
"... Abstract. The classification problem is considered in which an output variable y assumes discrete values with respective probabilities that depend upon the simultaneous values of a set of input variables x ={x1,...,xn}.At issue is how error in the estimates of these probabilities affects classificat ..."
Abstract - Cited by 248 (1 self) - Add to MetaCart
classification error when the estimates are used in a classification rule. These effects are seen to be somewhat counter intuitive in both their strength and nature. In particular the bias and variance components of the estimation error combine to influence classification in a very different way than

Bayesian Modeling of Uncertainty in Low-Level Vision

by Richard Szeliski , 1990
"... The need for error modeling, multisensor fusion, and robust algorithms i becoming increasingly recognized in computer vision. Bayesian modeling is a powerful, practical, and general framework for meeting these requirements. This article develops a Bayesian model for describing and manipulating the d ..."
Abstract - Cited by 204 (17 self) - Add to MetaCart
models using Bayes ' rule. We show how to compute optimal estimates from the posterior model and also how to compute the uncertainty (variance) in these estimates. To demonstrate the utility of our Bayesian framework, we present three examples of its application to real vision problems. The first

Large-scale bayesian logistic regression for text categorization

by Alexander Genkin, David D. Lewis, David Madigan - Technometrics
"... Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications. We present a simple Bayesian logistic regression approach that uses a Laplace prior to avoid overfitt ..."
Abstract - Cited by 191 (13 self) - Add to MetaCart
Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications. We present a simple Bayesian logistic regression approach that uses a Laplace prior to avoid

Using Bayesian Priors to Combine Classifiers for Adaptive Filtering

by unknown authors
"... An adaptive information filtering system monitors a document stream to identify the documents that match information needs specified by user profiles. As the system filters, it also refines its knowledge about the user’s information needs based on long-term observations of the document stream and pe ..."
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prior. This technique provides a trade off between bias and variance, and the combined classifier may achieve a consistent good performance at different stages of filtering. We implemented the proposed technique to combine two complementary classification algorithms: Rocchio and logistic regression

Magnetic resonance image tissue classification using a partial volume model

by David W. Shattuck, Stephanie R. Sandor-Leahy, Kirt A. Schaper, David A. Rottenberg, Richard M. Leahy - NEUROIMAGE , 2001
"... We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for imag ..."
Abstract - Cited by 137 (6 self) - Add to MetaCart
each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue

Order Statistics Combiners For Neural Classifiers

by Kagan Tumer, Joydeep Ghosh - In Proceedings of the World Congress on Neural Networks , 1995
"... : Several researchers have shown that linearly combining outputs of multiple neural classifiers results in better performance for many applications. In this paper we introduce a family of order statistics combiners as an alternative to linear combiners. We show analytically that the selection of the ..."
Abstract - Cited by 18 (8 self) - Add to MetaCart
of the median, the maximum and in general, the i th order statistic improves classification performance. Specifically, we show that order statistics combiners reduce the variance of the actual decision boundaries around the optimum boundary, and that this is directly related to classification error. 1

Feitosa, “A new covariance estimate for Bayesian classifiers in biometric recognition

by Carlos E. Thomaz, Duncan F. Gillies, Raul Q. Feitosa - IEEE Trans. Circuits Syst. Video Technol , 2004
"... Abstract—In many biometric pattern-recognition problems, the number of training examples per class is limited, and consequently the sample group covariance matrices often used in parametric and nonparametric Bayesian classifiers are poorly estimated or singular. Thus, a considerable amount of effort ..."
Abstract - Cited by 13 (4 self) - Add to MetaCart
Abstract—In many biometric pattern-recognition problems, the number of training examples per class is limited, and consequently the sample group covariance matrices often used in parametric and nonparametric Bayesian classifiers are poorly estimated or singular. Thus, a considerable amount
Results 1 - 10 of 378