AI News, Artificial Neural Networks/Competitive Learning
- On Thursday, October 4, 2018
- By Read More
Artificial Neural Networks/Competitive Learning
Competitive learning is a rule based on the idea that only one neuron from a given iteration in a given layer will fire at a time.
The “winner” of each iteration, element i* , is the element whose total weighted input is the largest.
Neurons become trained to be individual feature detectors, and a combination of feature detectors can be used to identify large classes of features from the input space.
ORIGINAL ARTICLEAdaptive competitive learning neural networks
In this paper, the adaptive competitive learning (ACL) neural network algorithm is proposed.
The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters.
Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.
Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data.
Accordingly, the individual neurons of the network learn to specialize on ensembles of similar patterns and in so doing become 'feature detectors' for different classes of input patterns.
The fact that competitive networks recode sets of correlated inputs to one of a few output neurons essentially removes the redundancy in representation which is an essential part of processing in biological sensory systems.
Thus, as more data are received, each node converges on the centre of the cluster that it has come to represent and activates more strongly for inputs in this cluster and more weakly for inputs in other clusters.
- On Wednesday, February 26, 2020
What is SELF-ORGANIZING MAP? What does SELF-ORGANIZING MAP mean? SELF-ORGANIZING MAP meaning
What is SELF-ORGANIZING MAP? What does SELF-ORGANIZING MAP mean? SELF-ORGANIZING MAP meaning - SELF-ORGANIZING MAP definition ...
How to Predict Stock Prices Easily - Intro to Deep Learning #7
We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use ...
How to Make an Image Classifier - Intro to Deep Learning #6
We're going to make our own Image Classifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the ...
Lecture 05 - Training Versus Testing
Training versus Testing - The difference between training and testing in mathematical terms. What makes a learning model able to generalize? Lecture 5 of 18 of ...
Lecture 3 | Loss Functions and Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...
Lecture 14 | Deep Reinforcement Learning
In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to ...
How to Generate Art - Intro to Deep Learning #8
We're going to learn how to use deep learning to convert an image into the style of an artist that we choose. We'll go over the history of computer generated art, ...
Data Clustering-Competitive Learning-Matlab Example
Competitive Learning Matlab Example • Divide a set of input patterns in 3 clusters that are inherent to the input data. • Unsupervised learning procedure ...
On-Device Machine Intelligence with Neural Projections
Deep neural networks and other machine learning models have been transformative for building intelligent systems capable of visual recognition, speech and ...