Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Part I Machine learning and kernel vector spaces
- Part II Dimension-reduction: PCA/KPCA and feature selection
- Part III Unsupervised learning models for cluster analysis
- Part IV Kernel ridge regressors and variants
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- 13 Efficient kernel methods for learning and classification
- Part VII Kernel methods and statistical estimation theory
- Part VIII Appendices
- References
- Index
13 - Efficient kernel methods for learning and classification
from Part VI - Kernel methods for green machine learning technologies
Published online by Cambridge University Press: 05 July 2014
- Frontmatter
- Dedication
- Contents
- Preface
- Part I Machine learning and kernel vector spaces
- Part II Dimension-reduction: PCA/KPCA and feature selection
- Part III Unsupervised learning models for cluster analysis
- Part IV Kernel ridge regressors and variants
- Part V Support vector machines and variants
- Part VI Kernel methods for green machine learning technologies
- 13 Efficient kernel methods for learning and classification
- Part VII Kernel methods and statistical estimation theory
- Part VIII Appendices
- References
- Index
Summary
Introduction
Since the invention of integrated circuits in the 1950s, processing gates and memory storages on a chip have grown at an exponential rate. Major breakthroughs in wireless and internet technologies have further promoted novel information technologies (IT). New applications, such as cloud computing and green computing, will undoubtedly have profound impacts on everyone's daily life.
Machine learning will play a vital part in modern information technology, especially in the era of big data analysis. For the design of kernel-based machine learning systems, it is important to find suitable kernel functions that lead to an optimal tradeoff between design freedom and computational complexity, which involves very often a choice between the intrinsic-space and empirical-space learning models.
A successful deployment of a machine learning system hinges upon a well-coordinated co-design of algorithm and hardware. This chapter addresses practical design issues related to cost-effective learning or low-power prediction that are vital for green IT applications.
This chapter addresses the following topics concerning cost-effective system implementations both in the learning phase and in the prediction phase.
(i) In the internet-based IT era, a system designer must first decide where to process the bulk of the information: a local/private client (e.g. a mobile device) or a data center (e.g. “the cloud”). Section 13.2 addresses the pros and cons of both options. The choice of strategy depends on a delicate tradeoff between the computation and communication costs, among many other system design factors.[…]
- Type
- Chapter
- Information
- Kernel Methods and Machine Learning , pp. 421 - 456Publisher: Cambridge University PressPrint publication year: 2014