000 | 04096nam a22005295i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-3-540-73180-1 _2doi |
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050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
072 | 7 |
_aTBJ _2bicssc |
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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. |
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300 |
_aX, 436 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |