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001 978-3-540-73954-8
003 DE-He213
005 20161121231205.0
007 cr nn 008mamaa
008 100301s2007 gw | s |||| 0|eng d
020 _a9783540739548
_9978-3-540-73954-8
024 7 _a10.1007/978-3-540-73954-8
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
245 1 0 _aPerspectives of Neural-Symbolic Integration
_h[electronic resource] /
_cedited by Barbara Hammer, Pascal Hitzler.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
300 _aXIII, 319 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v77
505 0 _aStructured Data and Neural Networks -- Kernels for Strings and Graphs -- Comparing Sequence Classification Algorithms for Protein Subcellular Localization -- Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank -- Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties -- Markovian Bias of Neural-based Architectures With Feedback Connections -- Time Series Prediction with the Self-Organizing Map: A Review -- A Dual Interaction Perspective for Robot Cognition: Grasping as a “Rosetta Stone” -- Logic and Neural Networks -- SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference -- The Core Method: Connectionist Model Generation for First-Order Logic Programs -- Learning Models of Predicate Logical Theories with Neural Networks Based on Topos Theory -- Advances in Neural-Symbolic Learning Systems: Modal and Temporal Reasoning -- Connectionist Representation of Multi-Valued Logic Programs.
520 _aThe human brain possesses the remarkable capability of understanding, interpreting, and producing language, structures, and logic. Unlike their biological counterparts, artificial neural networks do not form such a close liason with symbolic reasoning: logic-based inference mechanisms and statistical machine learning constitute two major and very different paradigms in artificial intelligence with complementary strengths and weaknesses. Modern application scenarios in robotics, bioinformatics, language processing, etc., however require both the efficiency and noise-tolerance of statistical models and the generalization ability and high-level modelling of structural inference meachanisms. A variety of approaches has therefore been proposed for combining the two paradigms. This carefully edited volume contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks. It brings together a representative selection of results presented by some of the top researchers in the field, covering theoretical foundations, algorithmic design, and state-of-the-art applications in robotics and bioinformatics.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aHammer, Barbara.
_eeditor.
700 1 _aHitzler, Pascal.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540739531
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v77
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-73954-8
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c509888
_d509888