LIBLINEAR -- A Library for Large Linear Classification Machine Learning Group at National Taiwan University Contributors We recently released LibShortText, a library for short-text classification and analysis. It's built upon LIBLINEAR. Version 1.94 released on November 12, 2013. An experimental version using 64-bit int is in LIBSVM tools. We are interested in large sparse regression data. A practical guide to LIBLINEAR is now available in the end of LIBLINEAR paper. Some extensions of LIBLINEAR are at LIBSVM Tools. LIBLINEAR is the winner of ICML 2008 large-scale learning challenge (linear SVM track). Introduction LIBLINEAR is a linear classifier for data with millions of instances and features. L2-regularized classifiers L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR) L1-regularized classifiers (after version 1.4) L2-loss linear SVM and logistic regression (LR) L2-regularized support vector regression (after version 1.9) L2-loss linear SVR and L1-loss linear SVR. Main features of LIBLINEAR include FAQ is here R.
Thomas Weise Statistics How To Light and Matter: open-source physics textbooks Extras Who's Using the Books Adoptions of Light and Matter Colleges and Universities High Schools Parker School, Kamuela, HI (Matt Hamilton) Little Axe High School, Norman, Oklahoma (Michael Dean) Orion International Academy, Pasadena, CA (John Gaines's Introduction to Physics) Ballinger High School, Ballinger, TX (Mr. Adoptions of Mechanics University of British Columbia (Profs. Pine Tree High School, Longview, Texas (Blake Sansom's AP Physics C) Ballinger High School, Ballinger, TX (Mr. Adoptions of Simple Nature Regis College (Prof. Adoptions of Special Relativity University of Peradeniya (Prof. Adoptions of Conceptual Physics University of Cincinnati, Blue Ash (Prof. Adoptions of Discover Physics Ohlone College (Otis Walton's Physics 108) Harmony Academies St. Adoptions of Fundamentals of Calculus Bellingham Technical College (Anita Peng's Math 148) Within each section above, schools are listed in reverse chronological order of adoption. Alignment with California Standards
Multiclass Support Vector Machine | GPU Computing Founders At Work: Stories of Startups' Early Days Elementary Concepts in Statistics In this introduction, we will briefly discuss those elementary statistical concepts that provide the necessary foundations for more specialized expertise in any area of statistical data analysis. The selected topics illustrate the basic assumptions of most statistical methods and/or have been demonstrated in research to be necessary components of our general understanding of the "quantitative nature" of reality (Nisbett, et al., 1987). We will focus mostly on the functional aspects of the concepts discussed and the presentation will be very short. Further information on each of the concepts can be found in statistical textbooks. Recommended introductory textbooks are: Kachigan (1986), and Runyon and Haber (1976); for a more advanced discussion of elementary theory and assumptions of statistics, see the classic books by Hays (1988), and Kendall and Stuart (1979). What are Variables? Variables are things that we measure, control, or manipulate in research. Correlational vs. Dependent vs.
Em em is a package which enables to create Gaussian Mixture Models (diagonal and full covariance matrices supported), to sample them, and to estimate them from data using Expectation Maximization algorithm. It can also draw confidence ellipsoides for multivariate models, and compute the Bayesian Information Criterion to assess the number of clusters in the data. In a near future, I hope to add so-called online EM (ie recursive EM) and variational Bayes implementation. em is implemented in python, and uses the excellent numpy and scipy packages. Numpy is a python packages which gives python a fast multi-dimensional array capabilities (ala matlab and the likes); scipy leverages numpy to build common scientific features for signal processing, linear algebra, statistics, etc... The toolbox depends on several packages to work: numpyscipysetuptoolsmatplotlib (if you wish to use the plotting facilities: this is not mandatory) Since July 2007, the toolbox is included in the learn scikits (scikits).