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1 - Fundamentals of kernel-based machine learning

from Part I - Machine learning and kernel vector spaces

Published online by Cambridge University Press:  05 July 2014

S. Y. Kung
Affiliation:
Princeton University, New Jersey
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Summary

Introduction

The rapid advances in information technologies, in combination with today's internet technologies (wired and mobile), not only have profound impacts on our daily lifestyles but also have substantially altered the long-term prospects of humanity. In this era of big data, diversified types of raw datasets with huge data-size are constantly collected from wired and/or mobile devices/sensors. For example, in Facebook alone, more than 250 million new photos are being added on a daily basis. The amount of newly available digital data more than doubles every two years. Unfortunately, such raw data are far from being “information” useful for meaningful analysis unless they are processed and distilled properly. The main purpose of machine learning is to convert the wealth of raw data into useful knowledge.

Machine learning is a discipline concerning the study of adaptive algorithms to infer from training data so as to extract critical and relevant information. It offers an effective data-driven approach to data mining and intelligent classification/prediction. The objective of learning is to induce optimal decision rules for classification/prediction or to extract the salient characteristics of the underlying system which generates the observed data. Its potential application domains cover bioinformatics, DNA expression and sequence analyses, medical diagnosis and health monitoring, brain–machine interfaces, biometric recognition, security and authentication, robot/computer vision, market analysis, search engines, and social network association.

In machine learning, the learned knowledge should be represented in a form that can readily facilitate decision making in the classification or prediction phase.

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Publisher: Cambridge University Press
Print publication year: 2014

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