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Latent Dirichlet allocation

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. For example, an LDA model might have topics that can be classified as CAT_related and DOG_related. Each document is assumed to be characterized by a particular set of topics. Model[edit] With plate notation, the dependencies among the many variables can be captured concisely. is the topic distribution for document i, The 1. , where .

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.

Information theory Overview[edit] The main concepts of information theory can be grasped by considering the most widespread means of human communication: language. Two important aspects of a concise language are as follows: First, the most common words (e.g., "a", "the", "I") should be shorter than less common words (e.g., "roundabout", "generation", "mediocre"), so that sentences will not be too long. Such a tradeoff in word length is analogous to data compression and is the essential aspect of source coding. Second, if part of a sentence is unheard or misheard due to noise — e.g., a passing car — the listener should still be able to glean the meaning of the underlying message. Such robustness is as essential for an electronic communication system as it is for a language; properly building such robustness into communications is done by channel coding. Note that these concerns have nothing to do with the importance of messages. Historical background[edit] With it came the ideas of This is justified because

CRF Project Page Gambling and information theory Statistical inference might be thought of as gambling theory applied to the world around. The myriad applications for logarithmic information measures tell us precisely how to take the best guess in the face of partial information.[1] In that sense, information theory might be considered a formal expression of the theory of gambling. It is no surprise, therefore, that information theory has applications to games of chance.[2] Kelly Betting[edit] Kelly betting or proportional betting is an application of information theory to investing and gambling. Part of Kelly's insight was to have the gambler maximize the expectation of the logarithm of his capital, rather than the expected profit from each bet. Side information[edit] where Y is the side information, X is the outcome of the betable event, and I is the state of the bookmaker's knowledge. The nature of side information is extremely finicky. Doubling rate[edit] Doubling rate in gambling on a horse race is [3] where there are (e.g if the where

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. · 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. · Learning for Text Categorization.

Entropy (information theory) 2 bits of entropy. A single toss of a fair coin has an entropy of one bit. A series of two fair coin tosses has an entropy of two bits. This definition of "entropy" was introduced by Claude E. Entropy is a measure of unpredictability of information content. Now consider the example of a coin toss. English text has fairly low entropy. If a compression scheme is lossless—that is, you can always recover the entire original message by decompressing—then a compressed message has the same quantity of information as the original, but communicated in fewer characters. Shannon's theorem also implies that no lossless compression scheme can compress all messages. Named after Boltzmann's H-theorem, Shannon defined the entropy H (Greek letter Eta) of a discrete random variable X with possible values {x1, ..., xn} and probability mass function P(X) as: Here E is the expected value operator, and I is the information content of X.[8][9] I(X) is itself a random variable. . The average uncertainty , with

ls | About Binomial options pricing model Use of the model[edit] The Binomial options pricing model approach is widely used as it is able to handle a variety of conditions for which other models cannot easily be applied. This is largely because the BOPM is based on the description of an underlying instrument over a period of time rather than a single point. As a consequence, it is used to value American options that are exercisable at any time in a given interval as well as Bermudan options that are exercisable at specific instances of time. Although computationally slower than the Black–Scholes formula, it is more accurate, particularly for longer-dated options on securities with dividend payments. For options with several sources of uncertainty (e.g., real options) and for options with complicated features (e.g., Asian options), binomial methods are less practical due to several difficulties, and Monte Carlo option models are commonly used instead. Method[edit] STEP 1: Create the binomial price tree[edit] or and ). , we have:

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. 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. Before I get on with the nitty gritty, it's best for you to forget everything you may already know about neural networks! Network Architecture For the purposes of this tutorial I'll be discussing a two dimensional SOM. Figure 2 A simple Kohonen network. V1, V2, V3...Vn W1, W2, W3...Wn Figure 3 1 2 3 4 5 Home

Value: The Third Factor Of Investing A stock's valuation is the final factor of the Fama-French three-factor model of investment returns. A stock's valuation is measured on a continuum from "value" to "growth." In broad strokes, value stocks are cheap and growth stocks are expensive. Consider a local utility company whose stock is selling for $10 a share. This company has a price per earnings (P/E) ratio of 10. In contrast, consider a technology startup company that has shown meteoric growth in the past three years. Investors might rightly decide that the growing technology company is worth more than the static regional utility. The P/E ratio is one common measurement used to place stocks on the value to growth continuum. Some measurements use the past four quarters of earnings, which is often called the trailing P/E ratio. Is this author on the ball? Follow and be the first to know when they publish. Follow Marotta on Money (111 followers) Investment advisor, portfolio strategy, long-term horizon

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. Rodeo: Sparse, greedy, nonparametric regression with Larry WassermanAnn. Most methods for estimating sparse undirected graphs for real-valued data in high dimensional problems rely heavily on the assumption of normality.

Learn to Trade Forex (Currencies), Stocks, & CFDs | InformedTrades 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

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