Projects matching python. About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes: -Complete refactoring of inner parts of the library. The code is easier to understand/modify and it allow simpler integration with new algorithms. -Updated to the latest version of NLOPT (2.4.1). -Error codes replaced with exceptions in C++ interface. -API modified to support new learning methods for kernel hyperparameters (e.g: MCMC). -Added configuration of random numbers (can be fixed for debugging). -Improved numerical results (e.g.: hyperparameter optimization is done in log space) -More examples and tests. -Fixed bugs.
All entries. Shogun | A Large Scale Machine Learning Toolbox. Orange - Data Mining Fruitful & Fun. Machine learning in Python — scikit-learn v0.9 documentation. PyBrain. Videos This video presentation was shown at the ICML Workshop for Open Source ML Software on June 25, 2010.
It explains some of the features and algorithms of PyBrain and gives tutorials on how to install and use PyBrain for different tasks. This video shows some of the learning features in PyBrain in action. Algorithms We implemented many useful standard and advanced algorithms in PyBrain, and in some cases created interfaces to existing libraries (e.g. LIBSVM). Supervised Learning Back-PropagationR-PropSupport-Vector-Machines (LIBSVM interface) Evolino Unsupervised Learning K-Means ClusteringPCA/pPCALSH for Hamming and Euclidean SpacesDeep Belief Networks Reinforcement Learning Value-based Q-Learning (with/without eligibility traces)SARSANeural Fitted Q-iteration Policy Gradients REINFORCENatural Actor-Critic Exploration Methods Epsilon-Greedy Exploration (discrete)Boltzmann Exploration (discrete)Gaussian Exploration (continuous)State-Dependent Exploration (continuous) Black-box Optimization Tools. Projects:lasvm [Léon Bottou] 1. Introduction LASVM is an approximate SVM solver that uses online approximation.
It reaches accuracies similar to that of a real SVM after performing a single sequential pass through the training examples. Further benefits can be achieved using selective sampling techniques to choose which example should be considered next. As show in the graph, LASVM requires considerably less memory than a regular SVM solver. This becomes a considerable speed advantage for large training sets. In fact LASVM has been used to train a 10 class SVM classifier with 8 million examples on a single processor.
See the LaSVM paper for the details. 2. We provide a complete implementation of LASVM under the well known GNU Public License. This source code contains a small C library implementing the kernel cache and the basic process and reprocess operations. These programs can handle three data file format: LIBSVM/SVMLight files These files represent examples using a simple text format. Binary files Split files. Multiclass Support Vector Machine | GPU Computing. Incremental training of support vector machines. BibTeX @ARTICLE{Shilton05incrementaltraining, author = {A. Shilton and M. Palaniswami and Senior Member and D.
Ralph and A. C. Bookmark OpenURL Abstract Abstract — We propose a new algorithm for the incremental training of Support Vector Machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Citations. Www.stat.cornell.edu/~li/reports/HashLearning.pdf. Www.bme.ogi.edu/~lantian/bibo/feature selection/J020_svm_feature_selection!.pdf. Www.cs.washington.edu/education/courses/cse590q/04au/papers/Sarawagi02.pdf. Abstract. Abstract. Www.cs.utexas.edu/~ml/papers/marlin-tr-02.pdf. Pages.cs.wisc.edu/~beechung/papers/record-matching.vldb07.final.pdf. Research.microsoft.com/pubs/63995/03-marlin-kdd.pdf. Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition. MILK: MACHINE LEARNING TOOLKIT — milk 0.3.7 documentation. Cheese Shop : tfclassify 0.1.2.
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). Python module for extended Infomax ICA. 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. Following the recent change of LIBSVM, we slightly adjust the way class labels are handled internally.
By default labels are ordered by their first occurrence in the training set. Hence for a set with -1/+1 labels, if -1 appears first, then internally -1 becomes +1. This has caused confusion. 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. Main features of LIBLINEAR include FAQ is here R. PcSVM. Package Index > pcSVM > pre 1.0 Not Logged In pcSVM pre 1.0 pcSVM is a framework for support vector machines pcSVM is a framwork for support vector machines.
Support Vector Machines is a new generation of learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition. Support Vector Machines outperform other classifiers as artificial neural networks in most situations. The core of the framework is written in c++, I used boost.python for python support.
Downloads (All Versions): 0 downloads in the last day 0 downloads in the last week 0 downloads in the last month Website maintained by the Python community Real-time CDN by Fastly / hosting by Rackspace / design by Tim Parkin. LIBSVM -- A Library for Support Vector Machines. LIBSVM -- A Library for Support Vector Machines Chih-Chung Chang and Chih-Jen Lin Version 3.20 released on November 15, 2014. It conducts some minor fixes. LIBSVM tools provides many extensions of LIBSVM. We now have a nice page LIBSVM data sets providing problems in LIBSVM format. A practical guide to SVM classification is available now! To see the importance of parameter selection, please see our guide for beginners. Using libsvm, our group is the winner of IJCNN 2001 Challenge (two of the three competitions), EUNITE world wide competition on electricity load prediction, NIPS 2003 feature selection challenge (third place), WCCI 2008 Causation and Prediction challenge (one of the two winners), and Active Learning Challenge 2010 (2nd place).
Introduction LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). Our goal is to help users from other fields to easily use SVM as a tool. 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).