Track My Order
Help Centre
1300 187 187
Likes
My Wish ListsLogin / Join
Booktopia
Australia's local bookstore

0 Item

Statistical and Neural Classifiers : An Integrated Approach to Design - Sarunas Raudys

eTEXT

Statistical and Neural Classifiers

An Integrated Approach to Design

By: Sarunas Raudys

eText | 6 December 2012

At a Glance

eText

$169.00

or 4 interest-free payments of $42.25 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Read online on
Desktop
Tablet
Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Automatic (machine) recognition, description, classification, and groupings of patterns are important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. Given a pattern, its recognition/classification may consist of one of the following two tasks: (1) supervised classification (also called discriminant analysis); the input pattern is assigned to one of several predefined classes, (2) unsupervised classification (also called clustering); no pattern classes are defined a priori and patterns are grouped into clusters based on their similarity. Interest in the area of pattern recognition has been renewed recently due to emerging applications which are not only challenging but also computationally more demanding (e. g. , bioinformatics, data mining, document classification, and multimedia database retrieval). Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have received increased attention. Neural networks and statistical pattern recognition are two closely related disciplines which share several common research issues. Neural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation. On the other hand, neural networks can derive benefit from some well-known results in statistical pattern recognition.

Read online on
Desktop
Tablet
Mobile

You Can Find This eBook In

Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceComputer VisionPattern RecognitionImage Processing

More in Computer Vision

This product is categorised by