000 04096nam a22005295i 4500
001 978-3-540-73180-1
003 DE-He213
005 20161121230542.0
007 cr nn 008mamaa
008 100301s2008 gw | s |||| 0|eng d
020 _a9783540731801
_9978-3-540-73180-1
024 7 _a10.1007/978-3-540-73180-1
_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 _aCase-Based Reasoning on Images and Signals
_h[electronic resource] /
_cedited by Petra Perner.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _aX, 436 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 ;
_v73
505 0 _ato Case-Based Reasoning for Signals and Images -- Similarity -- Distance Function Learning for Supervised Similarity Assessment -- Induction of Similarity Measures for Case Based Reasoning Through Separable Data Transformations -- Graph Matching -- Memory Structures and Organization in Case-Based Reasoning -- Learning a Statistical Model for Performance Prediction in Case-Based Reasoning -- A CBR Agent for Monitoring the Carbon Dioxide Exchange Rate from Satellite Images -- Extracting Knowledge from Sensor Signals for Case-Based Reasoning with Longitudinal Time Series Data -- Prototypes and Case-Based Reasoning for Medical Applications -- Case-Based Reasoning for Image Segmentation by Watershed Transformation -- Similarity-Based Retrieval for Biomedical Applications -- Medical Imagery in Case-Based Reasoning -- Instance-Based Relevance Feedback in Image Retrieval Using Dissimilarity Spaces.
520 _aThis book is the first edited book that deals with the special topic of signals and images within Case-Based Reasoning (CBR). Signal-interpreting systems are becoming increasingly popular in medical, industrial, ecological, biotechnological and many other applications. Existing statistical and knowledge-based techniques lack robustness, accuracy and flexibility. New strategies are needed that can adapt to changing environmental conditions, signal variation, user needs and process requirements. Introducing CBR strategies into signal-interpreting systems can satisfy these requirements. CBR can be used to control the signal-processing process in all phases of a signal-interpreting system to derive information of the highest possible quality. Beyond this CBR offers different learning capabilities, for all phases of a signal-interpreting system, that satisfy different needs during the development process of a signal-interpreting system. The structure of the book is divided into a theoretical part and into an application-oriented part. Scientists and computer science experts from industry, medicine and biotechnology who like to work on the special topics of CBR for signals and images will find this work useful. Although case-based reasoning is often not a standard lecture at universities we hope we to also inspire PhD students to deal with this topic.
650 0 _aEngineering.
650 0 _aMultimedia information systems.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aMultimedia Information Systems.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
700 1 _aPerner, Petra.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540731788
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v73
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-73180-1
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c500473
_d500473