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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. 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 Networks Tools

Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is tried and tested open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j. Download and installDocsCoursesBook

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. -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. -The number of inner iterations have been increased by default, so overall optimization time using default configuration might be slower, but with improved results. boost your Machine Learning projects - Project Web Hosting - Open Source Software 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. 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. <line> = <target><feature>:<value> ... The target value and each of the feature/value pairs are separated by a space character. Binary files Binary files take less space and load faster. Split files

Shark Machine Learning Library Incremental training of support vector machines BibTeX @ARTICLE{Shilton05incrementaltraining, author = {A. Shilton and M. Palaniswami and Senior Member and D. 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 Surrogate model A surrogate model is an engineering method used when an outcome of interest cannot be easily directly measured, so a model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the air flow around the wing for different shape variables (length, curvature, material, ..). For many real world problems, however, a single simulation can take many minutes, hours, or even days to complete. As a result, routine tasks such as design optimization, design space exploration, sensitivity analysis and what-if analysis become impossible since they require thousands or even millions of simulation evaluations. Goals[edit] The accuracy of the surrogate depends on the number and location of samples (expensive experiments or simulations) in the design space. 1. 1.

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. 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 When to use LIBLINEAR but not LIBSVM Download LIBLINEAR The package includes the source code in C/C++. R. Interfaces to LIBLINEAR