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LIBSVM FAQ. Feature selection. In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction.

Feature selection

The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Weka is a collection of machine learning algorithms for data mining tasks.

Weka 3 - Data Mining with Open Source Machine Learning Software in Java

The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. Q-Learning By Examples. By Kardi Teknomo Share this: Google+ In this tutorial, you will discover step by step how an agent learns through training without teacher in unknown environment.

Q-Learning By Examples

Reinforcement learning is training paradigm for agents in which we have example of problems but we do not have the immediate exact answer. Sample code for Q-learning. Www.acm.uiuc.edu/sigart/docs/QLearning.pdf. Reinforcement Learning - Algorithms. The parameters used in the Q-value update process are: - the learning rate, set between 0 and 1.

Reinforcement Learning - Algorithms

Setting it to 0 means that the Q-values are never updated, hence nothing is learned. Introduction to Reinforcement Learning. Xin Chen.

Introduction to Reinforcement Learning

University of Hawaii. Fall 2006. DBSCAN.M - dmfa07 - MATLAB code for dbscan - Data Mining projects for the class CIS4930 Fall 2007, Data Mining with Sanjay Ranka. AGHC.m - Classical data mining algorithm matlab c - Source Codes Reader - HackChina. K-Means Clustering Tutorial: Matlab Code. By Kardi Teknomo, PhD.

K-Means Clustering Tutorial: Matlab Code

Purchase the latest e-book with complete code of this k means clustering tutorial here For you who like to use Matlab, Matlab Statistical Toolbox contain a function name kmeans. If you do not have the statistical toolbox, you may use my generic code below. Kmeans: Matlab Code | Nirmal's Blog. My implementation of K means algorithm is highly customized.

Kmeans: Matlab Code | Nirmal's Blog

Initial cluster centroid can be selected in various of ways. Those are: • Randomly initialized cluster centroid as one of the data row. • Select first 3 data row was the three cluster center. • Provide the cluster centroid as a parameter, it is specially helpful when you want to perform the cluster with the same initial data centers so that we don’t have to worry about K means naming different to the same cluster in different run. Contents.m - Classical data mining algorithm matlab c - Source Codes Reader - HackChina. K_means.m - Classical data mining algorithm matlab c - Source Codes Reader - HackChina. Sponser links:

k_means.m - Classical data mining algorithm matlab c - Source Codes Reader - HackChina

Project

Backpropagation. The project describes teaching process of multi-layer neural network employing backpropagation algorithm.

Backpropagation

To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used: Each neuron is composed of two units. First unit adds products of weights coefficients and input signals. The second unit realise nonlinear function, called neuron activation function. FLD - Fisher Linear Discriminant. FLD - Fisher Linear Discriminant Let us assume we have sets , these represent classes, each containing elements (

FLD - Fisher Linear Discriminant

Research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf. Www.physics.ohio-state.edu/~gan/teaching/spring04/Chapter5.pdf. CS 229: Machine Learning. CS340 Winter 2010. Lectures MWF 4.00-5.00, Dempster 301 Calendar entry Prerequisites: Linear algebra, calculus, probability theory, programming (Matlab). Tutorial T2A F 3.00-4.00, Dempster 101 Tutorial T2B M 11.00-12.00, Dempster 101 Instructor: Arnaud Doucet. Office hours: Monday 5.00-6.000. Stochastic Gradient Descent. (1 votes, average: 3.00 out of 5) Loading ... For curve fitting using linear regression, there exists a minor variant of Batch Gradient Descent algorithm, called Stochastic Gradient Descent. In the Batch Gradient Descent, the parameter vector is updated as, (loop over all elements of training set in one iteration) N'est pas accessible. Machine Learning 10-701/15-781. Batch Gradient Descent. I happened to stumble on Prof. Andrew Ng’s Machine Learning classes which are available online as part of Stanford Center for Professional Development.

The first lecture in the series discuss the topic of fitting parameters for a given data set using linear regression. For understanding this concept, I chose to take data from the top 50 articles of this blog based on the pageviews in the month of September 2011. Decision Tree. ID3 Decision Trees in Java. ID3 Decision Trees in Java In a previous post, I explored how one might apply decision trees to solve a complex problem.

This post will explore the code necessary to implement that decision tree. If you would like a full copy of the source code, it is available here in zip format. Entropy.java – In Entropy.java, we are concerned with calculating the amount of entropy, or the amount of uncertainty or randomness with a particular variable. For example, consider a classifier with two classes, YES and NO.