Author: Akash Kurup

Learning Vector Quantization

Learning vector quantization (LVQ), is a prototype-based supervised classification algorithm. LVQ is the supervised counterpart of vector quantization systems. LVQ can be understood as a special case of an artificial...

Simple Competitive Learning Networks

In competitive networks, output units compete for the right to respond. Goal: method of clustering – divide the data into a number of clusters such that the inputs in...

Kohonen Self Organizing Networks

Kohonen’s networks are one of basic types of self-organizing neural networks. The ability to self-organize provides new possibilities – adaptation to formerly unknown input data. It seems to be...

Radial Basis Function Network

A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier. Generally, when people talk...

Adaline Neural Network

ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The network...

The back-propagation Algorithm – a mathematical approach

Units are connected to one another. Connections correspond to the edges of the underlying directed graph. There is a real number associated with each connection, which is called the...

Applications of neural networks

Neural Networks in Practice Given this description of neural networks and how they work, what real world applications are they suited for? Neural networks have broad applicability to real...

The Back-Propagation Algorithm

In order to train a neural network to perform some task, we must adjust the weights of each unit in such a way that the error between the desired...

Transfer Function

The behaviour of an ANN (Artificial Neural Network) depends on both the weights and the input-output function (transfer function) that is specified for the units. This function typically falls...

The Learning Process

The memorisation of patterns and the subsequent response of the network can be categorised into two general paradigms: associative mapping in which the network learns to produce a particular...