000			04692nam a22006255i 4500
001			978-3-540-37952-2
003			DE-He213
005			20240730182650.0
007			cr nn 008mamaa
008			100301s2006 gw | s |||| 0|eng d
020			_a9783540379522 _9978-3-540-37952-2
024	7		_a10.1007/11829898 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
245	1	0	_aArtificial Neural Networks in Pattern Recognition _h[electronic resource] : _bSecond IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings / _cedited by Friedhelm Schwenker, Simone Marinai.
250			_a1st ed. 2006.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2006.
300			_aX, 302 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 Artificial Intelligence, _x2945-9141 ; _v4087
505	0		_aUnsupervised Learning -- Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions -- Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition -- Adaptive Feedback Inhibition Improves Pattern Discrimination Learning -- Semi-supervised Learning -- Supervised Batch Neural Gas -- Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes -- On the Effects of Constraints in Semi-supervised Hierarchical Clustering -- A Study of the Robustness of KNN Classifiers Trained Using Soft Labels -- Supervised Learning -- An Experimental Study on Training Radial Basis Functions by Gradient Descent -- A Local Tangent Space Alignment Based Transductive Classification Algorithm -- Incremental Manifold Learning Via Tangent Space Alignment -- A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware -- Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks -- Support Vector Learning -- Support Vector Regression Using Mahalanobis Kernels -- Incremental Training of Support Vector Machines Using Truncated Hypercones -- Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques -- Multiple Classifier Systems -- Multiple Classifier Systems for Embedded String Patterns -- Multiple Neural Networks for Facial Feature Localization in Orientation-Free Face Images -- Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory -- Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization -- Visual Object Recognition -- Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks -- Visual Classification of Images by Learning Geometric Appearances Through Boosting -- An Eye Detection System Based on Neural Autoassociators -- Orientation Histograms for Face Recognition -- Data Mining in Bioinformatics -- An Empirical Comparison of Feature Reduction Methods in the Context of Microarray Data Classification -- Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data -- Learning and Feature Selection Using the Set Covering Machine with Data-Dependent Rays on Gene Expression Profiles.
650		0	_aArtificial intelligence. _93407
650		0	_aPattern recognition systems. _93953
650		0	_aApplication software. _9128843
650		0	_aComputer science. _99832
650		0	_aElectronic data processing _xManagement. _9128844
650		0	_aBioinformatics. _99561
650	1	4	_aArtificial Intelligence. _93407
650	2	4	_aAutomated Pattern Recognition. _931568
650	2	4	_aComputer and Information Systems Applications. _9128845
650	2	4	_aTheory of Computation. _9128846
650	2	4	_aIT Operations. _931703
650	2	4	_aBioinformatics. _99561
700	1		_aSchwenker, Friedhelm. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9128847
700	1		_aMarinai, Simone. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9128848
710	2		_aSpringerLink (Online service) _9128849
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783540379515
776	0	8	_iPrinted edition: _z9783540828280
830		0	_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v4087 _9128850
856	4	0	_uhttps://doi.org/10.1007/11829898
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
912			_aZDB-2-LNC
942			_cELN
999			_c91454 _d91454