Self-organizing map (SOM): The self-organizing map belongs to a class of unsupervised learning that is based on competitive learning, in which output neurons compete amongst themselves to be activated, with the result that only one is activated at any one time. This activated neuron is called the winning neuron. Such competition can be induced/implemented by having lateral inhibition connections (negative feedback paths) between the neurons, resulting in the neurons organizing themselves. SOM can be imagined as a sheet-like neural network, with nodes arranged as regular, usually two-dimensional grids. The principal goal of a SOM is to transform an incoming arbitrary dimensional signal into a one- or two-dimensional discrete map, and to perform this transformation adaptively in a topologically ordered fashion. The neurons are selectively tuned to various input patterns (stimuli) or classes of input patterns during the course of the competitive learning. The locations of the...
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You're reading from Practical Machine Learning Cookbook
Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
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Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.
Read more about Atul Tripathi