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 |