000 04208nam a22005415i 4500
001 978-1-4614-1903-7
003 DE-He213
005 20140220083244.0
007 cr nn 008mamaa
008 120223s2012 xxu| s |||| 0|eng d
020 _a9781461419037
_9978-1-4614-1903-7
024 7 _a10.1007/978-1-4614-1903-7
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aDai, Honghua.
_eeditor.
245 1 0 _aReliable Knowledge Discovery
_h[electronic resource] /
_cedited by Honghua Dai, James N. K. Liu, Evgueni Smirnov.
264 1 _aBoston, MA :
_bSpringer US,
_c2012.
300 _aXVIII, 308p. 77 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aTransductive 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.
520 _aReliable 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.
650 0 _aComputer science.
650 0 _aDatabase management.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aDatabase Management.
650 2 4 _aPattern Recognition.
650 2 4 _aData Storage Representation.
650 2 4 _aComputer Graphics.
700 1 _aLiu, James N. K.
_eeditor.
700 1 _aSmirnov, Evgueni.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461419020
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-1903-7
912 _aZDB-2-SCS
999 _c101156
_d101156