PUBLIC MARKS with tags "machine learning" & "bayesian networks"

The 24th Annual International Conference on Machine Learning is being held in conjunction with the 2007 International Conference on Inductive Logic Programming at Oregon State University in Corvallis, Oregon. As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive.

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].