| 書目名稱 | Similarity-Based Clustering | | 副標(biāo)題 | Recent Developments | | 編輯 | Michael Biehl,Barbara Hammer,Thomas Villmann | | 視頻video | http://file.papertrans.cn/868/867412/867412.mp4 | | 叢書名稱 | Lecture Notes in Computer Science | | 圖書封面 |  | | 描述 | Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which | | 出版日期 | Book 2009 | | 關(guān)鍵詞 | Extension; algorithms; bioinformatics; biology; classification; feature selection; learning; machine learni | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-642-01805-3 | | isbn_softcover | 978-3-642-01804-6 | | isbn_ebook | 978-3-642-01805-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 | | issn_series | 0302-9743 | | copyright | Springer-Verlag Berlin Heidelberg 2009 |
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