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. 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 is a collection of machine learning algorithms for data mining tasks. 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.
by Kardi Teknomo In this tutorial, you will discover step by step how an agent learns through training without teacher (unsupervised) in unknown environment. You will find out part of reinforcement learning algorithm called Q-learning . Reinforcement learning algorithm has been widely used for many applications such as robotics, multi agent system, game, motion planning, navigation, and etc.
The parameters used in the Q-value update process are: - the learning rate, set between 0 and 1. Setting it to 0 means that the Q-values are never updated, hence nothing is learned.
Xin Chen. University of Hawaii. Fall 2006
By Kardi Teknomo, PhD. < Previous | Next | Contents > 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 .
The project describes teaching process of multi-layer neural network employing backpropagation algorithm. 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 Let us assume we have sets , these represent classes, each containing elements (
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.
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) For Stochastic Gradient Descent , the vector gets updated as, at each iteration the algorithm goes over only one among
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.