Competitive Learning: Fundamentals and Applications for Reinforcement Learning through Competition

What Is Competitive Learning

In artificial neural networks, competitive learning is a type of unsupervised learning in which nodes fight for the right to respond to a subset of the input data. This type of learning is known as "competitive learning." Competitive learning is a form of learning that is similar to Hebbian learning. It operates by raising the level of specialization at each node in the network. It works quite well for discovering clusters hidden within data.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Competitive Learning

Chapter 2: Self-organizing map

Chapter 3: Perceptron

Chapter 4: Unsupervised Learning

Chapter 5: Hebbian Theory

Chapter 6: Backpropagation

Chapter 7: Multilayer Perceptron

Chapter 8: Learning Rule

Chapter 9: Feature Learning

Chapter 10: Types of artificial neural networks

(II) Answering the public top questions about competitive learning.

(III) Real world examples for the usage of competitive learning in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of competitive learning.

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