Lecture-11: Machine Learning: Perceptrons- Kernel Perceptron Learning Part-3/4

SANJEEV SHARMA, 12th Nov 2010: Machine Learning: Lecture-11: Kernel Perceptron Learning.

CONTENTS: Simple Perceptron Algorithm, Voted Perceptron Algorithm, Kenrel Perceptron Algorithm.

DESCRIPTION: To sovle a machine learning problem like classification and regression requires constructing basis functions. In general it's quite hard to determine what kind of basis fucntions will be able to perform well in the task at hand. Sometimes we may find that the polynomial function can perform well, but what should be the degree of the polynomial? Using kernels circumvent this problem. The cardinal advantage of using Kernels is that it obviates the necessicity of constructing the basis functions explicitly. In this Lecture I address this issue and explain the simple perceptron learning algorithm with linear basis functions and then the voted version of the perceptron learning algorithm, again with the linear basis functions. The voted version assigns weight to each of the weight vector that it encounters during the learning phase and then outputs the final weight vector that is the voted-sum of the weight vectors. However perceptron can solve nonliner problems by constructing the non-linear basis functions. But using the KERNEL PERCEPTRON algorithm obviates the need to construct the basis functins.

Category:

Science & Technology

Tags:

• Kernel Perceptron Learning
• Machine Learning Lecture
• Searching-Eye