000 03929nam a22005295i 4500
001 978-3-540-47568-2
003 DE-He213
005 20160624102024.0
007 cr nn 008mamaa
008 121227s1993 gw | s |||| 0|eng d
020 _a9783540475682
_9978-3-540-47568-2
024 7 _a10.1007/3-540-56483-7
_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
245 1 0 _aMachine Learning: From Theory to Applications
_h[electronic resource] :
_bCooperative Research at Siemens and MIT /
_cedited by Stephen José Hanson, Werner Remmele, Ronald L. Rivest.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1993.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c1993.
300 _aVIII, 276 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v661
505 0 _aStrategic directions in machine learning -- Training a 3-node neural network is NP-complete -- Cryptographic limitations on learning Boolean formulae and finite automata -- Inference of finite automata using homing sequences -- Adaptive search by learning from incomplete explanations of failures -- Learning of rules for fault diagnosis in power supply networks -- Cross references are features -- The schema mechanism -- L-ATMS: A tight integration of EBL and the ATMS -- Massively parallel symbolic induction of protein structure/function relationships -- Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks -- Phoneme discrimination using connectionist networks -- Behavior-based learning to control IR oven heating: Preliminary investigations -- Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks.
520 _aThis volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aProcessor Architectures.
700 1 _aHanson, Stephen José.
_eeditor.
700 1 _aRemmele, Werner.
_eeditor.
700 1 _aRivest, Ronald L.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540564836
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v661
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-56483-7
942 _2EBK6112
_cEBK
999 _c35406
_d35406