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PLSI: The True Fisher Kernel and beyond IID Processes, Information Matrix and Model Identification in PLSI

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Fisher Kernels and Probabilistic Latent Semantic Models

Emmanuel Eckard

Tasks that rely on semantic content of documents, notably Information Retrieval and Document Classification, can benefit from a good account of document context, i.e. the semantic association between documents. To this effect, the scheme of latent semantic ...

EPFL2010

PLSI: the true Fisher Kernel and beyond

Jean-Cédric Chappelier, Emmanuel Eckard

The Probalistic Latent Semantic Indexing model, introduced by T. Hofmann (1999), has engendered applications in numerous fields, notably document classification and information retrieval. In this context, the Fisher kernel was found to be an appropriate do ...

2009

Rôle de la matrice d'information et pondération des composantes dans les noyaux de Fisher pour PLSI

Jean-Cédric Chappelier, Emmanuel Eckard

ABSTRACT. An information-geometric approach for document similarities in the framework of “Probabilistic Latent Semantic Indexing” was ﬁrst proposed by T. Hofmann (2000) and later extended (“revisited”) by Nyffenegger et al. (2006). This paper presents an ...

2009

Utilisation de PLSI en recherche d'information

Jean-Cédric Chappelier, Emmanuel Eckard

The PLSI model (“Probabilistic Latent Semantic Indexing”) offers a document indexing scheme based on probabilistic latent category models. It entailed applications in diverse ﬁelds, notably in information retrieval (IR). Nevertheless, PLSI cannot process d ...

2009

An Ad Hoc Information Retrieval Perspective on PLSI through language model identification

Jean-Cédric Chappelier, Emmanuel Eckard

Ten years ago, PLSI opened the road to probabilistic latent semantic representations of documents. It led to a number of applications in different ﬁelds, including ad hoc Information Retrieval. However, inherent limitations hinder its use on documents not ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2009

Machine learning for information retrieval

David Grangier

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

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Machine Learning for Information Retrieval

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École Polytechnique Fédérale de Lausanne2008

Machine Learning for Information Retrieval

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IDIAP2008

A Discriminative Kernel-based Model to Rank Images from Text Queries

Samy Bengio, David Grangier

This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The ...

2008

Modeling semantic aspects for cross-media image indexing

Daniel Gatica-Perez, Florent Monay Michaud

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content ...

2007