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020 _a9783642018053
_9978-3-642-01805-3
024 7 _a10.1007/978-3-642-01805-3
_2doi
050 4 _aQH301-705
072 7 _aPSA
_2bicssc
072 7 _aSCI086000
_2bisacsh
072 7 _aPSA
_2thema
082 0 4 _a570
_223
245 1 0 _aSimilarity-Based Clustering
_h[electronic resource] :
_bRecent Developments and Biomedical Applications /
_cedited by Thomas Villmann, M. Biehl, Barbara Hammer, Michel Verleysen.
250 _a1st ed. 2009.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2009.
300 _aXI, 203 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 ;
_v5400
505 0 _aI: Dynamics of Similarity-Based Clustering -- Statistical Mechanics of On-line Learning -- Some Theoretical Aspects of the Neural Gas Vector Quantizer -- Immediate Reward Reinforcement Learning for Clustering and Topology Preserving Mappings -- II: Information Representation -- Advances in Feature Selection with Mutual Information -- Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data -- Median Topographic Maps for Biomedical Data Sets -- Visualization of Structured Data via Generative Probabilistic Modeling -- III: Particular Challenges in Applications -- Learning Highly Structured Manifolds: Harnessing the Power of SOMs -- Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images -- HIV-1 Drug Resistance Prediction and Therapy Optimization: A Case Study for the Application of Classification and Clustering Methods.
520 _aThis book is the outcome of the Dagstuhl Seminar on "Similarity-Based Clustering" held at Dagstuhl Castle, Germany, in Spring 2007. In three chapters, the three fundamental aspects of a theoretical background, the representation of data and their connection to algorithms, and particular challenging applications are considered. Topics discussed concern a theoretical investigation and foundation of prototype based learning algorithms, the development and extension of models to directions such as general data structures and the application for the domain of medicine and biology. Similarity based methods find widespread applications in diverse application domains, including biomedical problems, but also in remote sensing, geoscience or other technical domains. The presentations give a good overview about important research results in similarity-based learning, whereby the character of overview articles with references to correlated research articles makes the contributions particularly suited for a first reading concerning these topics.
650 0 _aLife sciences.
650 0 _aBioinformatics.
650 0 _aMedicine
_xResearch.
650 0 _aBiology
_xResearch.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 1 4 _aLife Sciences.
650 2 4 _aComputational and Systems Biology.
650 2 4 _aBiomedical Research.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
700 1 _aVillmann, Thomas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBiehl, M.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aHammer, Barbara.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aVerleysen, Michel.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783642018046
776 0 8 _iPrinted edition:
_z9783642018060
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v5400
856 4 0 _uhttps://doi.org/10.1007/978-3-642-01805-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
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