Kohonen's Self Organizing Feature Maps Introductory Note This tutorial is the first of two related to self organising feature maps. Initially, this was just going to be one big comprehensive tutorial, but work demands and other time constraints have forced me to divide it into two. Nevertheless, part one should provide you with a pretty good introduction. SOM tutorial part 1
Robert Schapire's Home Page Princeton University Department of Computer Science 35 Olden Street Princeton, NJ 08540 Tel: 609-258-7726 Fax: 609-258-1771
Home Page of Thorsten Joachims · International Conference on Machine Learning (ICML), Program Chair (with Johannes Fuernkranz), 2010. · Journal of Machine Learning Research (JMLR) (action editor, 2004 - 2009). · Machine Learning Journal (MLJ) (action editor). · Journal of Artificial Intelligence Research (JAIR) (advisory board member). · Data Mining and Knowledge Discovery Journal (DMKD) (action editor, 2005 - 2008). · Special Issue on Learning to Rank for IR, Information Retrieval Journal, Hang Li, Tie-Yan Liu, Cheng Xiang Zhai, T.
Ashutosh Saxena - Assistant Professor - Cornell - Computer Scien Saxena's Robot Learning Lab projects were featured in BBC World News. Vaibhav Aggarwal was awarded ELI'14 research award for his work with Ashesh Jain. Koppula's video on reactive robotic response was the finalist for best video award at IROS 2013. Ashesh Jain's NIPS'13 paper on learning preferences in trajectories was mentioned in Discovery Channel Daily Planet, Techcrunch, FOX News, NBC News and several others. (Watch the video!)
In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003. Topics in LDA In LDA, each document may be viewed as a mixture of various topics. This is similar to probabilistic latent semantic analysis (pLSA), except that in LDA the topic distribution is assumed to have a Dirichlet prior.
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Popular Ensemble Methods: An Empirical Study Journal of Artificial Intelligence Research11 (1999), pp. 169-198. Submitted 1/99; published 8/99. © 1999 AI Access Foundation and Morgan Kaufmann Publishers.
My research is in machine learning and statistics, with basic research on theory, methods, and algorithms. Areas of focus include nonparametric methods, sparsity, the analysis of high-dimensional data, graphical models, information theory, and applications in language processing, computer vision, and information retrieval. Perspectives on several research topics in statistical machine learning appeared in this Statistica Sinica commentary. This work has received support from NSF, ARDA, DARPA, AFOSR, and Google. Some sample projects: John Lafferty
Next: Introduction Active Learning with Statistical Models David A. Cohn Zoubin Ghahramani Michael I. Jordan Center for Biological and Computational Learning Dept. of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 USA Active Learning with Statistical Models
Amos Storkey - Research - Belief Networks Belief Networks and Probabilistic Graphical Models Belief networks (Bayes Nets, Bayesian Networks) are a vital tool in probabilistic modelling and Bayesian methods. They are one class of probabilistic graphical model. In other words they are a marriage between two important fields: probability theory and graph theory.
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The Non-linearity and Complexity Research Group has high international visibility in the areas of pattern analysis, probabilistic methods, non-linear dynamics and the application of methods from statistical physics to the analysis of complex systems. The underpinning methodology used includes principled approaches from probabilistic modelling, Bayesian statistics, statistical mechanics and non-linear stochastic and deterministic differential equations. Particularly significant application domains include Biomedical Information Engineering and Signal Processing, Health Informatics, Environmental Modelling and Weather Forecasting, Error-correcting Codes and Multi-user Communication, Complex Systems and Networks, Solitons and Optical Fibers, and Chaos and turbulence. Neural Computing Research Group: The GTM H
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The term "Pareto principle" can also refer to Pareto efficiency. The Pareto principle (also known as the 80–20 rule, the law of the vital few, and the principle of factor sparsity) states that, for many events, roughly 80% of the effects come from 20% of the causes. Business-management consultant Joseph M. Juran suggested the principle and named it after Italian economist Vilfredo Pareto, who observed in 1906 that 80% of the land in Italy was owned by 20% of the population; Pareto developed the principle by observing that 20% of the pea pods in his garden contained 80% of the peas. It is a common rule of thumb in business; e.g., "80% of your sales come from 20% of your clients". Mathematically, the 80-20 rule is roughly followed by a power law distribution (also known as a Pareto distribution) for a particular set of parameters, and many natural phenomena have been shown empirically to exhibit such a distribution.