Reliable Knowledge Discovery / edited by Honghua Dai, James N. K. Liu, Evgueni Smirnov.

Format:

Book

Contributor:

Dai, Honghua, editor.

Liu, J. N. K. (James N. K.), editor.

Smirnov, Evgueni, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Database management.

Pattern perception.

Data structures (Computer science).

Computer graphics.

Artificial Intelligence.

Database Management.

Pattern Recognition.

Data Storage Representation.

Computer Graphics.

Local Subjects:

Artificial Intelligence.

Database Management.

Pattern Recognition.

Data Storage Representation.

Computer Graphics.

Physical Description:

1 online resource (XVIII, 310 pages)

Edition:

First edition 2012.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer New York : Imprint: Springer, 2012.

System Details:

text file PDF

Summary:

Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military. Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters. Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.

Contents:

Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining

Estimating Reliability for Assessing and Correcting Individual Streaming Predictions

Error Bars for Polynomial Neural Networks

Robust-Diagnostic Regression: A Prelude for Inducing Reliable Knowledge from Regression

Reliable Graph Discovery

Combining Version Spaces and Support Vector Machines for Reliable Classification

Reliable Ticket Routing in Expert Networks

Reliable Aggregation on Network Traffic for Web Based Knowledge Discovery

Sensitivity and Generalization of SVM with Weighted and Reduced Features

Reliable Gesture Recognition with Transductivie Confidence Machines

Reliability in A Feature-Selection Process for Intrusion Detection

The Impact of Sample Size and Data Quality to Classification Reliability

A Comparative Analysis of Instance-based Penalization Techniques for Classification

Subsequence Frequency Measurement and its Impact on Reliability of Knowledge Discovery in Single Sequences

Improving Reliability of Unbalanced Text Mining by Reducing Performance Bias

Formal Representation and Verification of Ontology Using State Controlled Coloured Petri Nets

A Reliable System Platform for Group Decision Support under Uncertain Environments

Index.

Other Format:

Printed edition:

ISBN:

978-1-4614-1903-7

9781461419037

Access Restriction:

Restricted for use by site license.