書目名稱 | Machine Learning and Interpretation in Neuroimaging | 副標(biāo)題 | International Worksh | 編輯 | Georg Langs,Irina Rish,Brian Murphy | 視頻video | http://file.papertrans.cn/621/620483/620483.mp4 | 概述 | State-of-the-art contributions.Interdisciplinary research.Unique visibility | 叢書名稱 | Lecture Notes in Computer Science | 圖書封面 |  | 描述 | Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light | 出版日期 | Conference proceedings 2012 | 關(guān)鍵詞 | classification; data mining; fMRI; multivariate encoding; multivariate pattern analysis (MVPA) | 版次 | 1 | doi | https://doi.org/10.1007/978-3-642-34713-9 | isbn_softcover | 978-3-642-34712-2 | isbn_ebook | 978-3-642-34713-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 | issn_series | 0302-9743 | copyright | Springer-Verlag Berlin Heidelberg 2012 |
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