000 04137nam a22004575i 4500
001 978-1-4419-8020-5
003 DE-He213
005 20140220083233.0
007 cr nn 008mamaa
008 120412s2012 xxu| s |||| 0|eng d
020 _a9781441980205
_9978-1-4419-8020-5
024 7 _a10.1007/978-1-4419-8020-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aSayed-Mouchaweh, Moamar.
_eeditor.
245 1 0 _aLearning in Non-Stationary Environments
_h[electronic resource] :
_bMethods and Applications /
_cedited by Moamar Sayed-Mouchaweh, Edwin Lughofer.
264 1 _aNew York, NY :
_bSpringer New York,
_c2012.
300 _aXII, 440p. 158 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPrologue -- Part I: Dynamic Methods for Unsupervised Learning Problems -- Incremental Statistical Measures -- A Granular Description of Data: A Study in Evolvable Systems -- Incremental Spectral Clustering -- Part II: Dynamic Methods for Supervised Classification Problems -- Semi-Supervised Dynamic Fuzzy K-Nearest Neighbors -- Making Early Predictions of the Accuracy of Machine Learning Classifiers -- Incremental Classifier Fusion and its Applications in Industrial Monotiroing and Diagnostics -- Instance-Based Classification and Regression on Data Streams -- Part III: Dynamic Methods for Supervised Regression Problems -- Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++) -- Sequential Adaptive Fuzzy Inference System for Function Approximation Problems -- Interval Approach for Evolving Granular System Modeling -- Part IV: Applications of Learning in Non-Stationary Environments -- Dynamic Learning in Multiple Time-Series in a Non-Stationary Environmenty -- Optimizing Feature Calculation in Adaptive Machine Vision Systems -- On-line Quality Contol with Flexible Evolving Fuzzy Systems -- Identification of a Class of Hybrid Dynamic Systems.
520 _aRecent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.   Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy.   Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations.   This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.  
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aOptical pattern recognition.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aPattern Recognition.
700 1 _aLughofer, Edwin.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441980199
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-8020-5
912 _aZDB-2-ENG
999 _c100550
_d100550