Hashing. Ai. Ica. SOM tutorial part 1. 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. Certainly more than enough to whet your appetite anyway! I will appreciate any feedback you are willing to give - good or bad. Overview Kohonen Self Organising Feature Maps, or SOMs as I shall be referring to them from now on, are fascinating beasts. A common example used to help teach the principals behind SOMs is the mapping of colours from their three dimensional components - red, green and blue, into two dimensions. Figure 1 Screenshot of the demo program (left) and the colours it has classified (right). One of the most interesting aspects of SOMs is that they learn to classify data without supervision.
Network Architecture Figure 2. Robert Schapire's Home Page. 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.
Joachims, Springer, 2009. · Special Issue on Automated Text Categorization, Journal on Intelligent Information Systems, T. . · Special Issue on Text-Mining, Zeitschrift Künstliche Intelligenz, Vol. 2, 2002. · Enriching Information Retrieval, P. . · Redundancy, Diversity, and Interdependent Document Relevance (IDR), P. . · Beyond Binary Relevance, P. . · Machine Learning for Web Search, D. . · Learning to Rank for Information Retrieval, T. . · Learning in Structured Output Spaces, U. Open Source Computer Vision Library. Ashutosh Saxena - Assistant Professor - Cornell - Computer Scien.
See our workshop at RSS'14: Planning for Robots: Learning vs Humans. Our 5th RGB-D workshop at RSS'14: Vision vs Robotics! Our special issue on autonomous grasping and manipulation is out! Saxena's Robot Learning Lab projects were featured in BBC World News. Daily Beast comments about Amazon's predictive delivery and Saxena's predictive robots.
Zhaoyin Jia's paper on physics-based reasoning for RGB-D image segmentation, an oral at CVPR'13, is now conditionally accepted in IEEE TPAMI. 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. Saxena gave invited talks at the AI-based Robotics, at the Caging for manipulation, and at the Developmental and Social Robotics workshops at IROS 2013. Prof. Prof. Prof. Latent Dirichlet allocation. 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] Topics in LDA[edit] 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. In practice, this results in more reasonable mixtures of topics in a document. For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. The 1. . Welcome to The Machine Learning Forum. CRF Project Page. Ls | About. Machinelearning.org - Home. Popular Ensemble Methods: An Empirical Study. John Lafferty. 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: Rodeo: Sparse, greedy, nonparametric regression with Larry WassermanAnn. Statist., Vol. 36, No. 1 (2008), pp 28-63. Most methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality. 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. It is this combination which makes them a powerful methodology within machine learning and statistics. Use of belief networks has become widespread partly because of their intuitive appeal. Introduction to Bayesian Methods Although belief networks are a tool of probability theory, their most common use is within the framework of Bayesian analysis. In order to infer anything from data, we must have and use prior information. One implication of these beliefs is that there is no indisputable way of obtaining knowledge from data. The Bayesian approach to problems can be summed up in this simple way: Belief Networks A belief network is a directed graph.
Inference in Belief Networks. Henry Rowleys Home Page. Neural Computing Research Group: The GTM H. I - Home. Pareto principle. The Pareto Principle asserts that only a "vital few" peapods produce the majority of peas. The Pareto principle (also known as the 80/20 rule, the law of the vital few, or the principle of factor sparsity)[1][2] states that, for many events, roughly 80% of the effects come from 20% of the causes.[3] Management consultant Joseph M. Juran suggested the principle and named it after Italian economist Vilfredo Pareto, who noted the 80/20 connection while at the University of Lausanne in 1896, as published in his first work, Cours d'économie politique. Essentially, Pareto showed that approximately 80% of the land in Italy was owned by 20% of the population. It is an axiom of business management that "80% of sales come from 20% of clients".[4] Richard Koch authored the book, The 80/20 Principle, which illustrated some practical applications of the Pareto principle in business management and life.[5] The Pareto principle is only tangentially related to Pareto efficiency.
In economics[edit]