PUBLIC MARKS with tags "machine learning" & svm

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.

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.

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].