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Probabilistic Latent Semantic Indexing

by Thomas Hofmann , 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
Abstract - Cited by 1225 (10 self) - Add to MetaCart
model is able to deal with domain-specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments

A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization

by Thorsten Joachims , 1997
"... The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the ..."
Abstract - Cited by 456 (1 self) - Add to MetaCart
in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classifier. The Rocchio classifier, its probabilistic variant, and a naive Bayes classifier are compared on six text categorization tasks

Mixtures of Probabilistic Principal Component Analysers

by Michael E. Tipping, Christopher M. Bishop , 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract - Cited by 532 (6 self) - Add to MetaCart
Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a

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
the variance for Naive-Bayes, which was very stable. We observed that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference. Voting variants, some of which are introduced in this paper, include: pruning versus no pruning, use of probabilistic

1 A Probabilistic Variant of Projection Temporal Logic

by Xiaoxiao Yang
"... ar ..."
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Probabilistic variants of Ulam’s game and many-valued logic

by C. Marini, F. Montagna
"... Abstract In this paper we discuss some generalizations of Ulam’s game with lies: some of them are simply probabilistic variants of it, some others differ from it by the presence of more than one number to guess. In the last part of the paper, we also discuss the relationship between such variants an ..."
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Abstract In this paper we discuss some generalizations of Ulam’s game with lies: some of them are simply probabilistic variants of it, some others differ from it by the presence of more than one number to guess. In the last part of the paper, we also discuss the relationship between such variants

A Simple Data-Adaptive Probabilistic Variant Calling Model

by Steve Hoffmann, Peter F. Stadler, Korbinian Strimmer , 2014
"... ar ..."
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Efficient noise-tolerant learning from statistical queries

by Michael Kearns - JOURNAL OF THE ACM , 1998
"... In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize a new but related model of learning from stat ..."
Abstract - Cited by 353 (5 self) - Add to MetaCart
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize a new but related model of learning from

Finding motifs using random projections

by Jeremy Buhler, Martin Tompa , 2001
"... Pevzner and Sze [23] considered a precise version of the motif discovery problem and simultaneously issued an algorithmic challenge: find a motif Å of length 15, where each planted instance differs from Å in 4 positions. Whereas previous algorithms all failed to solve this (15,4)-motif problem, Pevz ..."
Abstract - Cited by 285 (6 self) - Add to MetaCart
on simulated data demonstrate that this algorithm performs better than existing algorithms and, in particular, typically solves the difficult (14,4)-, (16,5)-, and (18,6)-motif problems quite efficiently. A probabilistic estimate shows that the small values of � for which the algorithm fails to recover

A generalisation, a simplification and some applications of Paillier's probabilistic public-key system

by Ivan Damgård, Mads Jurik - LNCS , 2001
"... We propose a generalisation of Paillier’s probabilistic public key system, in which the expansion factor is reduced and which allows to adjust the block length of the scheme even after the public key has been fixed, without loosing the homomorphic property.We show that the generalisation is as secu ..."
Abstract - Cited by 222 (2 self) - Add to MetaCart
We propose a generalisation of Paillier’s probabilistic public key system, in which the expansion factor is reduced and which allows to adjust the block length of the scheme even after the public key has been fixed, without loosing the homomorphic property.We show that the generalisation
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