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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999

"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."

Abstract - Cited by 13236 (32 self) - Add to MetaCart

Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

A generative/discriminative learning algorithm for image classification

by Yi Li, Linda G. Shapiro, Jeff A. Bilmes - In Proc. IEEE Intern. Conf. on Computer Vision, volume II , 2005

"... We have developed a two-phase generative / discriminative learning procedure for the recognition of classes of objects and concepts in outdoor scenes. Our method uses both multiple types of object features and context within the image. The generative phase normalizes the description length of images ..."

Abstract - Cited by 18 (2 self) - Add to MetaCart

We have developed a two-phase generative / discriminative learning procedure for the recognition of classes of objects and concepts in outdoor scenes. Our method uses both multiple types of object features and context within the image. The generative phase normalizes the description length

A Generative/Discriminative Learning Algorithm for Image Classification

by Yi Li Linda , 2005

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A Simple Discriminative Learning Algorithm for Context

by Stanley M. Bileschi, Lior Wolf, Tomaso Poggio

"... The Problem: To design a simple algorithm for learning appropriate context for still-image object detection tasks. Our approach will learn a computationally-inexpensive discriminating function to divide pixels likely to contain the target object from those which are not. Motivation: The role of cont ..."

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The Problem: To design a simple algorithm for learning appropriate context for still-image object detection tasks. Our approach will learn a computationally-inexpensive discriminating function to divide pixels likely to contain the target object from those which are not. Motivation: The role

Kernel-based Discriminative Learning Algorithms for Labeling Sequences, Trees, and Graphs

by Hisashi Kashima, Yuta Tsuboi - In Proceedings of ICML-2004 , 2004

"... We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses pointwise label prediction, large features, including arbitrary number of hidden variables, can be incorporated with po ..."

Abstract - Cited by 11 (0 self) - Add to MetaCart

We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses pointwise label prediction, large features, including arbitrary number of hidden variables, can be incorporated

A maximum margin discriminative learning algorithm for temporal signals

by Wenjie Xu, Jiankang Wu, Zhiyong Huang

"... We propose a new maximum margin discriminative learning algorithm here for classification of temporal signals. It is superior to conventional HMM in the sense that it does not need prior knowledge of the data distribution. It learns the classifier by using a nonlinear discriminative procedure based ..."

Abstract - Cited by 2 (0 self) - Add to MetaCart

We propose a new maximum margin discriminative learning algorithm here for classification of temporal signals. It is superior to conventional HMM in the sense that it does not need prior knowledge of the data distribution. It learns the classifier by using a nonlinear discriminative procedure based

A fast learning algorithm for deep belief nets

by Geoffrey E. Hinton, Simon Osindero - Neural Computation , 2006

"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."

Abstract - Cited by 970 (49 self) - Add to MetaCart

very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modelled by long ravines in the free

An introduction to kernel-based learning algorithms

by Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf - IEEE TRANSACTIONS ON NEURAL NETWORKS , 2001

"... This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and ..."

Abstract - Cited by 598 (55 self) - Add to MetaCart

This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and

The use of the area under the ROC curve in the evaluation of machine learning algorithms

by Andrew P. Bradley - PATTERN RECOGNITION , 1997

"... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Ne ..."

Abstract - Cited by 685 (3 self) - Add to MetaCart

In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k

On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes

by Andrew Y. Ng, Michael I. Jordan , 2001

"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."

Abstract - Cited by 520 (8 self) - Add to MetaCart

is increased, one in which each algorithm does better. This stems from the observation -- which is borne out in repeated experiments -- that while discriminative learning has lower asymptotic error, a generative classifier may also approach its (higher) asymptotic error much faster.

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