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 a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. Weka supports deep learning!
VLFeat - Home 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.
SourceForge.net: boost your Machine Learning projects - Project Web Hosting - Open Source Software Kernel Functions for Machine Learning Applications In recent years, Kernel methods have received major attention, particularly due to the increased popularity of the Support Vector Machines. Kernel functions can be used in many applications as they provide a simple bridge from linearity to non-linearity for algorithms which can be expressed in terms of dot products. In this article, we will list a few kernel functions and some of their properties. Many of these functions have been incorporated in Accord.NET, a extension framework for the popular AForge.NET Framework which also includes many other statistics and machine learning tools. Contents Kernel Methods Kernel methods are a class of algorithms for pattern analysis or recognition, whose best known element is the support vector machine (SVM). The main characteristic of Kernel Methods, however, is their distinct approach to this problem. The Kernel Trick Kernel Properties Choosing the Right Kernel Kernel Functions 1. The Linear kernel is the simplest kernel function. 2. 3. 4. 5. 6. 7. 8. 9.
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 Cooking with Computers - CwC Goals and motivations Can computers help cooking? Can "machine learning" help cooking? Can "neural networks" help cooking? Can "knowledge representation" help cooking? The Cooking with Computers workshop aims at gathering researchers from as many fields of AI as possible. Submission topics This workshop is widely open to all AI researchers, whatever their sub-topic of research is. Machine learning (categorizing cooking elements, e.g. what is a soup?