Concept

Classement automatique

Publications associées (608)

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How does a dictation machine recognize speech?

Hervé Bourlard

There is magic (or is it witchcraft?) in a speech recognizer that transcribes continuous radio speech into text with a word accuracy of even not more than 50%. The extreme difficulty of this task, tough, is usually not perceived by the general public. This ...

Idiap2008

Dictionary Learning Based Dimensionality Reduction for Classification

Pierre Vandergheynst, Karin Schnass

In this article we present a signal model for classification based on a low dimensional dictionary embedded into the high dimensional signal space. We develop an alternate projection algorithm to find the embedding and the dictionary and finally test the c ...

2008

On Quality of Quality Measures for Classification

Andrzej Drygajlo, Krzysztof Kryszczuk

In this paper we provide a theoretical discussion of the in)pact of uncertainty in quality measurement oil the expected benefits of including biometric signal quality measures in classification. While all ideal signal quality measure should be a, precise q ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Support Vector Machines with a Reject Option

We consider the problem of binary classification where the classifier may abstain instead of classifying each observation. The Bayes decision rule for this setup, known as Chow’s rule, is deﬁned by two thresholds on posterior probabilities. From simple des ...

2008

Biologically Motivated Audio-Visual Cue Integration for Object

Barbara Caputo, Jie Luo

Auditory and visual cues are important sensor inputs for biological and artificial systems. They provide crucial information for navigating environments, recognizing categories, animals and people. How to combine effectively these two sensory channels is s ...

2008

Statistical Analysis of Impulse Radio Ultra-Wideband Multi User Interference based on Measurements

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

Multiuser interference (MUI) statistical models for impulse radio ultra-wideband (IR-UWB) systems can be an important in providing an accurate estimate of the channel state. As such, it can have a major impact on the overall system performance. In the lite ...

IEEE2008

Decision Tree Learning for Drools

Gizil Oguz

Decision trees can be used to represent a large number of expert system rules in a compact way. We describe machine learning algorithms for learning decision trees. We have implemented the algorithms, including bagging and boosting techniques. We have depl ...

2008

SHAPE PRIOR BASED ON STATISTICAL MAP FOR ACTIVE CONTOUR SEGMENTATION

Jean-Philippe Thiran, Valérie Duay, Alia Lemkaddem, Nawal Houhou

We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the proba ...

2008

Discriminative cue integration for medical image annotation

Barbara Caputo, Tatiana Tommasi

Automatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a mu ...

2008