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lasvm [Léon Bottou]

by ogrisel (via)
LASVM is an approximate SVM solver that uses online approximation. It reaches accuracies similar to that of a real SVM after performing a single sequential pass through the training examples. Further benefits can be achieved using selective sampling techniques to choose which example should be considered next. As show in the graph, LASVM requires considerably less memory than a regular SVM solver. This becomes a considerable speed advantage for large training sets. In fact LASVM has been used to train a 10 class SVM classifier with 8 million examples on a single processor.

Téléchargement illégal L'appel de SVM contre la future loi Hadopi | SVM

by srcmax (via)

Premièrement, elles ne montrent de l’ensemble des internautes qu’une image caricaturale de pillards. Il existe déjà des lois pour punir la contrefaçon. Cette nouvelle loi ne va pas punir des pirates, elle va punir le public.

Nous disons que la surveillance des réseaux est inefficace et indigne d’une économie moderne.

Nous invitons les producteurs et ayants droit à s’adapter aux nouvelles façons de consommer plutôt que d’espérer en vain les juguler.


Elefant - What is Elefant

by ogrisel
Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning Elefant include modules for many common optimisation problems arising in machine learning and inference. It is designed to be modular and easy to use. Framework provides easy to use python interface, which can be use for quick prototyping and testing inference algorithms.

IBM Research | IBM Haifa Labs| Machine learning for healthcare (EuResist)

by ogrisel (via)
Generative-discriminative Hybrid Technique We plan to use a technique that combines two kinds of learning algorithms: discriminative and generative. We plan to employ Bayesian networks in the generative phase, and SVM in the discriminative phase. Algorithms under the generative framework try to find a statistical model that best represents the data. The predictions are then based on the likelihood scores derived from the model. This category includes algorithms such as Hidden Markov Models (HMM) [1], Gaussian Mixture Models (GMM) [2] and more complicated graphical models such as Bayesian networks [3].



by ramenian & 10 others
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