| 書(shū)目名稱 | Representation Discovery using Harmonic Analysis | | 編輯 | Sridhar Mahadevan | | 視頻video | http://file.papertrans.cn/828/827392/827392.mp4 | | 叢書(shū)名稱 | Synthesis Lectures on Artificial Intelligence and Machine Learning | | 圖書(shū)封面 |  | | 描述 | Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete | | 出版日期 | Book 2008 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01546-5 | | isbn_softcover | 978-3-031-00418-6 | | isbn_ebook | 978-3-031-01546-5Series ISSN 1939-4608 Series E-ISSN 1939-4616 | | issn_series | 1939-4608 | | copyright | Springer Nature Switzerland AG 2008 |
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