Concept

Perceptron

Publications associées (219)

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Analysis of MLP Based Hierarchical Phoneme Posterior Probability Estimator

Hervé Bourlard, Hynek Hermansky, Joel Praveen Pinto

We analyze a simple hierarchical architecture consisting of two multilayer perceptron (MLP) classifiers in tandem to estimate the phonetic class conditional probabilities. In this hierarchical setup, the first MLP classifier is trained using standard acous ...

2010

Enhanced Phone Posteriors for Improving Speech Recognition Systems

Hervé Bourlard, Hamed Ketabdar

Using phone posterior probabilities has been increasingly explored for improving automatic speech recognition (ASR) systems. In this paper, we propose two approaches for hierarchically enhancing these phone posteriors, by integrating long acoustic context, ...

2010

OM-2: An Online Multi-class Multi-kernel Learning Algorithm

Barbara Caputo, Jie Luo, Marco Fornoni

Efficient learning from massive amounts of information is a hot topic in computer vision. Available training sets contain many examples with several visual descriptors, a setting in which current batch approaches are typically slow and does not scale well. ...

Idiap2010

OM-2: An Online Multi-class Multi-kernel Learning Algorithm

Barbara Caputo, Jie Luo, Marco Fornoni

2010

Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

Joel Praveen Pinto

In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in ...

EPFL2010

Feature Selection using Stochastic Search: An Application to System Identification

Ian Smith, Sandro Saitta

System identification using multiple-model strategies may involve thousands of models with several parameters. However, only a few models are close to the correct model. A key task involves finding which parameters are important for explaining candidate mo ...

American Society of Civil Engineers2010

Online-Batch Strongly Convex Multi Kernel Learning

Barbara Caputo, Jie Luo

Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled ap- proach to combine multiple cues, and to obtain state-of-the- art performance. A general drawback of these strategies is the high computational co ...

Idiap2010

Online-Batch Strongly Convex Multi Kernel Learning

Barbara Caputo, Jie Luo

2010

A Priori Guided Reconstruction For Fdot Using Mixed Norms

Michaël Unser, Jean-Charles Baritaux

We propose new soft-prior approach for incorporating structural a priori in FDOT reconstruction. The technique is a non-linear regularization scheme based on the penalization of the (2, 1)-mixed norm. We solve the numerical reconstruction problem with an i ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

A priori Guided Reconstruction for FDOT Using Mixed Norms

Michaël Unser, Jean-Charles Baritaux

IEEE2010