Results 1 - 10 of 45,776

Semantic similarity based on corpus statistics and lexical taxonomy

by Jay J. Jiang, David W. Conrath - Proc of 10th International Conference on Research in Computational Linguistics, ROCLING’97 , 1997
"... This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantifie ..."
Abstract - Cited by 873 (0 self) - Add to MetaCart
quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content

Boosting the margin: A new explanation for the effectiveness of voting methods

by Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee - 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 ..."
Abstract - Cited by 897 (52 self) - Add to MetaCart
that techniques used in the analysis of Vapnik’s support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins

Vulnerabilities Analysis

by Matt Bishop , 1999
"... This note presents a new model for classifying vulnerabilities in computer systems. The model is structurally different than earlier models, It decomposes vulnerabilities into small parts, called "primitive conditions. " Our hypothesis is that by examining systems for these conditi ..."
Abstract - Cited by 557 (15 self) - Add to MetaCart
This note presents a new model for classifying vulnerabilities in computer systems. The model is structurally different than earlier models, It decomposes vulnerabilities into small parts, called "primitive conditions. " Our hypothesis is that by examining systems

Opinion Mining and Sentiment Analysis

by Bo Pang, Lillian Lee , 2008
"... An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, active ..."
Abstract - Cited by 749 (3 self) - Add to MetaCart
as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new

Indexing by latent semantic analysis

by Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, Richard Harshman - JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE , 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
Abstract - Cited by 3779 (35 self) - Add to MetaCart
A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries

Efficient and Effective Clustering Methods for Spatial Data Mining

by Raymond T. Ng, Jiawei Han , 1994
"... Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLARANS which ..."
Abstract - Cited by 709 (37 self) - Add to MetaCart
Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLARANS which

A review of image denoising algorithms, with a new one

by A. Buades, B. Coll, J. M. Morel - SIMUL , 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
Abstract - Cited by 508 (6 self) - Add to MetaCart
and, second, to propose a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image. The mathematical analysis is based on the analysis of the “method noise, ” defined as the difference between a digital image and its denoised version. The NL-means algorithm

Bayesian Analysis of Stochastic Volatility Models

by Eric Jacquier, Nicholas G. Polson, Peter E. Rossi , 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized ARCH ..."
Abstract - Cited by 601 (26 self) - Add to MetaCart
this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized

Nonlinear component analysis as a kernel eigenvalue problem

by Bernhard Schölkopf, Alexander Smola, Klaus-Robert Müller - , 1996
"... We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all ..."
Abstract - Cited by 1573 (83 self) - Add to MetaCart
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all

A new approach to the maximum flow problem

by Andrew V. Goldberg, Robert E. Tarjan - JOURNAL OF THE ACM , 1988
"... All previously known efficient maximum-flow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortest-length augmenting paths at once (using the layered network approach of Dinic). An alternative method based on the pre ..."
Abstract - Cited by 672 (33 self) - Add to MetaCart
All previously known efficient maximum-flow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortest-length augmenting paths at once (using the layered network approach of Dinic). An alternative method based
Results 1 - 10 of 45,776