On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
By:
Sign Up Now!
Already a Member? Log In
You must be logged into Bookshare to access this title.
Learn about membership options,
or view our freely available titles.
- Synopsis
- A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
- Copyright:
- 2012
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783642307522
- Related ISBNs:
- 9783642307515
- Publisher:
- Springer Berlin Heidelberg
- Date of Addition:
- 10/24/16
- Copyrighted By:
- Springer
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.