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.
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· 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.
Zhaoyin Jia's paper and Yun Jiang's paper were accepted as orals in CVPR'13. Saxena received NSF Career Award for research in robotic perception. Hema Koppula's paper on detecting human activities from RGB-D videos accepted in IJRR. Hallucinating Humans webpage is now up. Aerial Robotics webpage is now up.
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. [ 1 ] [ edit ] 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 .
Journal of Artificial Intelligence Research 11 (1999), pp. 169-198. Submitted 1/99; published 8/99. © 1999 AI Access Foundation and Morgan Kaufmann Publishers . All rights reserved. David Opitz Department of Computer Science University of Montana Missoula, MT 59812 USA firstname.lastname@example.org
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:
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
As an introduction to the research I have been doing using and developing belief network approaches, I thought it might be useful to provide a basic introduction to what I am talking about. If you find this tutorial useful then please put a link to it on your home page. To switch to a printable form of this document hit here . Reload to return to the original form.
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.
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. [ 1 ] [ 2 ] 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; he developed the principle by observing that 20% of the pea pods in his garden contained 80% of the peas. [ 2 ] It is a common rule of thumb in business; e.g., "80% of your sales come from 20% of your clients".