The Experts below are selected from a list of 77178 Experts worldwide ranked by ideXlab platform
Olli Simula - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - A learning vector quantization algorithm for Probabilistic Models
2000Co-Authors: Jaakko Hollmén, Volker Tresp, Olli SimulaAbstract:In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative Models through Bayes's rule. Sometimes, however, it is convenient to express the Models as Probabilistic Models, since they are generative in nature and can handle the representation of high-dimensional data like time-series. In this paper, we derive a discriminative training procedure based on Learning Vector Quantization (LVQ) where the codebook is expressed in terms of Probabilistic Models. The likelihood-based distance measure is justified using the Kullback-Leibler distance. In updating the winner unit, a gradient learning step is taken with regard to the parameters of the Probabilistic model. The method essentially departs from a prototypical representation and incorporates learning in the parameter space of generative Models. As an illustration, we present experiments in the fraud detection domain, where Models of calling behavior are used to classify mobile phone subscribers to normal and fraudulent users. This is an extension of our earlier work in clustering Probabilistic Models with the Self-Organizing Map (SOM) algorithm to the classification domain.
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A learning vector quantization algorithm for Probabilistic Models
2000 10th European Signal Processing Conference, 2000Co-Authors: Jaakko Hollmén, Volker Tresp, Olli SimulaAbstract:In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative Models through Bayes's rule. Sometimes, however, it is convenient to express the Models as Probabilistic Models, since they are generative in nature and can handle the representation of high-dimensional data like time-series. In this paper, we derive a discriminative training procedure based on Learning Vector Quantization (LVQ) where the codebook is expressed in terms of Probabilistic Models. The likelihood-based distance measure is justified using the Kullback-Leibler distance. In updating the winner unit, a gradient learning step is taken with regard to the parameters of the Probabilistic model. The method essentially departs from a prototypical representation and incorporates learning in the parameter space of generative Models. As an illustration, we present experiments in the fraud detection domain, where Models of calling behavior are used to classify mobile phone subscribers to normal and fraudulent users. This is an extension of our earlier work in clustering Probabilistic Models with the Self-Organizing Map (SOM) algorithm to the classification domain.
Steve Lawrence - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Models for unified collaborative and content based recommendation in sparse data environments
arXiv: Information Retrieval, 2013Co-Authors: Alexandrin Popescul, Lyle H Ungar, David M Pennock, Steve LawrenceAbstract:Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified Probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's [1999] aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. Global Probabilistic Models coupled with standard Expectation Maximization (EM) learning algorithms tend to drastically overfit in sparse-data situations, as is typical in recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the ResearchIndex library of Computer Science publications show that appropriate mixture Models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global Probabilistic Models also allow more general inferences than local methods like k-NN.
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Probabilistic Models for unified collaborative and content based recommendation in sparse data environments
Uncertainty in Artificial Intelligence, 2001Co-Authors: Alexandrin Popescul, Lyle H Ungar, David M Pennock, Steve LawrenceAbstract:Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified Probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmarm's (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However, global Probabilistic Models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the Researchlndex library of Computer Science publications show that appropriate mixture Models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global Probabilistic Models also allow more general inferences than local methods like k-NN.
Fernando Pereira - One of the best experts on this subject based on the ideXlab platform.
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conditional random fields Probabilistic Models for segmenting and labeling sequence data
International Conference on Machine Learning, 2001Co-Authors: John Lafferty, Andrew Mccallum, Fernando PereiraAbstract:We present conditional random fields , a framework for building Probabilistic Models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov Models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those Models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov Models (MEMMs) and other discriminative Markov Models based on directed graphical Models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting Models to HMMs and MEMMs on synthetic and natural-language data.
Jaakko Hollmén - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - A learning vector quantization algorithm for Probabilistic Models
2000Co-Authors: Jaakko Hollmén, Volker Tresp, Olli SimulaAbstract:In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative Models through Bayes's rule. Sometimes, however, it is convenient to express the Models as Probabilistic Models, since they are generative in nature and can handle the representation of high-dimensional data like time-series. In this paper, we derive a discriminative training procedure based on Learning Vector Quantization (LVQ) where the codebook is expressed in terms of Probabilistic Models. The likelihood-based distance measure is justified using the Kullback-Leibler distance. In updating the winner unit, a gradient learning step is taken with regard to the parameters of the Probabilistic model. The method essentially departs from a prototypical representation and incorporates learning in the parameter space of generative Models. As an illustration, we present experiments in the fraud detection domain, where Models of calling behavior are used to classify mobile phone subscribers to normal and fraudulent users. This is an extension of our earlier work in clustering Probabilistic Models with the Self-Organizing Map (SOM) algorithm to the classification domain.
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A learning vector quantization algorithm for Probabilistic Models
2000 10th European Signal Processing Conference, 2000Co-Authors: Jaakko Hollmén, Volker Tresp, Olli SimulaAbstract:In classification problems, it is preferred to attack the discrimination problem directly rather than indirectly by first estimating the class densities and by then estimating the discrimination function from the generative Models through Bayes's rule. Sometimes, however, it is convenient to express the Models as Probabilistic Models, since they are generative in nature and can handle the representation of high-dimensional data like time-series. In this paper, we derive a discriminative training procedure based on Learning Vector Quantization (LVQ) where the codebook is expressed in terms of Probabilistic Models. The likelihood-based distance measure is justified using the Kullback-Leibler distance. In updating the winner unit, a gradient learning step is taken with regard to the parameters of the Probabilistic model. The method essentially departs from a prototypical representation and incorporates learning in the parameter space of generative Models. As an illustration, we present experiments in the fraud detection domain, where Models of calling behavior are used to classify mobile phone subscribers to normal and fraudulent users. This is an extension of our earlier work in clustering Probabilistic Models with the Self-Organizing Map (SOM) algorithm to the classification domain.
Alexandrin Popescul - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Models for unified collaborative and content based recommendation in sparse data environments
arXiv: Information Retrieval, 2013Co-Authors: Alexandrin Popescul, Lyle H Ungar, David M Pennock, Steve LawrenceAbstract:Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified Probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's [1999] aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. Global Probabilistic Models coupled with standard Expectation Maximization (EM) learning algorithms tend to drastically overfit in sparse-data situations, as is typical in recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the ResearchIndex library of Computer Science publications show that appropriate mixture Models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global Probabilistic Models also allow more general inferences than local methods like k-NN.
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Probabilistic Models for unified collaborative and content based recommendation in sparse data environments
Uncertainty in Artificial Intelligence, 2001Co-Authors: Alexandrin Popescul, Lyle H Ungar, David M Pennock, Steve LawrenceAbstract:Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified Probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmarm's (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However, global Probabilistic Models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the Researchlndex library of Computer Science publications show that appropriate mixture Models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global Probabilistic Models also allow more general inferences than local methods like k-NN.
