Machine Learning Miniproject 1 Vlad Balan
نویسنده
چکیده
A Self-Organizing Map (SOM or Kohonen Map) is a method of mapping vectors of an n-dimensional space V into elements of a lower-dimensional subspace A having a discretized structure consisting of a lattice of so-called neurons, objects associated with vectors of the space V . We denote the with r ∈ A elements of A and with wr ∈ V their associated vectors. The SOM algorithm begins by assigning random values to the elements of A. During the training phase, the SOM is presented with sequences of training data vectors from V . Its purpose is to create a continuous structure embedded in V (an approximation of a smooth sub-manifold of V ). In order to achieve this goal, neighboring neurons have to be associated with vectors of close values. A neighborhood activation function hrs has to be selected, for example the Gaussian
منابع مشابه
Pattern Recognition and Machine Learning
In this note, we describe the sequential minimal optimization (SMO) algorithm to solve the soft margin support vector machine (SVM) binary classification problem, which is to be implemented as part of the miniproject. In order to understand our arguments here, the reader is advised to study the SVM chapter of the course notes, in particular the “Solving the Support Vector Machine” section (the ...
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