Results 1 - 10 of 11,341

The pyramid match kernel: Discriminative classification with sets of image features

by Kristen Grauman, Trevor Darrell - IN ICCV , 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
Abstract - Cited by 544 (29 self) - Add to MetaCart
Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

Optimal Brain Damage

by Yann Le Cun, John S. Denker, Sara A. Sola , 1990
"... We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved sp ..."
Abstract - Cited by 510 (5 self) - Add to MetaCart
speed of learning and/or classification. The basic idea is to use second-derivative information to make a tradeoff between network complexity and training set error. Experiments confirm the usefulness of the methods on a real-world application.

Support Vector Machine Active Learning with Applications to Text Classification

by Simon Tong , Daphne Koller - JOURNAL OF MACHINE LEARNING RESEARCH , 2001
"... Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based acti ..."
Abstract - Cited by 735 (5 self) - Add to MetaCart
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool

Distance metric learning for large margin nearest neighbor classification

by Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul - In NIPS , 2006
"... We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
Abstract - Cited by 695 (14 self) - Add to MetaCart
. On seven data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification—for example, achieving a test error rate of 1.3 % on the MNIST handwritten digits. As in support vector machines (SVMs), the learning problem reduces to a

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

by Thomas G. Dietterich , 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
Abstract - Cited by 723 (8 self) - Add to MetaCart
error). Two widely used statistical tests are shown to have high probability of type I error in certain situations and should never be used: a test for the difference of two proportions and a paired-differences t test based on taking several random train-test splits. A third test, a paired

Exact Matrix Completion via Convex Optimization

by Emmanuel J. Candès, Benjamin Recht , 2008
"... We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfe ..."
Abstract - Cited by 873 (26 self) - Add to MetaCart
We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can

High performance scalable image compression with EBCOT

by David Taubman - IEEE Trans. Image Processing , 2000
"... A new image compression algorithm is proposed, based on independent Embedded Block Coding with Optimized Truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich feature set, including resolution and SNR ..."
Abstract - Cited by 586 (11 self) - Add to MetaCart
A new image compression algorithm is proposed, based on independent Embedded Block Coding with Optimized Truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich feature set, including resolution and SNR

On the algorithmic implementation of multi-class kernel-based vector machines

by Koby Crammer, Yoram Singer, Nello Cristianini, John Shawe-taylor, Bob Williamson - Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
Abstract - Cited by 559 (13 self) - Add to MetaCart
objective function. Unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks, our notion of margin yields a direct method for training multiclass predictors. By using the dual of the optimization problem we are able

A Comparison of Methods for Multiclass Support Vector Machines

by Chih-Wei Hsu, Chih-Jen Lin - IEEE TRANS. NEURAL NETWORKS , 2002
"... Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary class ..."
Abstract - Cited by 952 (22 self) - Add to MetaCart
larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such “all-together” methods. We then compare their performance with three methods based on binary classifications: “one-against-all,” “one
Results 1 - 10 of 11,341