This handbook presents a thorough overview in 45 chapters from more than 100 renowned experts in the field. It provides the tools to help overcome the problems of video storage, cataloging, and retrieval, by exploring content standardization and other content clasSification and analysis methods. The challenge of these complex problems make this book a must-have for video database practitioners in the fields of image and video processing, computer vision, multimedia systems, data mining, and many other diverse disciplines. Topics include video segmentation and summarization, archiving and retrieval, and modeling and representation.
ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. clasSification Algorithms include Naive Bayes, KNN
Support Vector Machine is small sample method based on statistic learning theory. It is a new method to deal with the highly nonlinear clasSification and regression problems .It can better deal with the small sample, nonlinear and
AdaBoost is an efficient tool in machine learning. It can combine a series of weak learners into a strong learner. Besides pattern clasSification, it also can be applied into feature selection. This document explains the use of AdaBoost.
模式識(shí)別學(xué)習(xí)綜述.該論文的英文參考文獻(xiàn)為303篇.很有可讀價(jià)值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern clasSification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
用Fourier變換求取信號(hào)的功率譜---周期圖法
用Fourier變換求取信號(hào)的功率譜---分段周期圖法
用Fourier變換求取信號(hào)的功率譜---welch方法
功率譜估計(jì)----多窗口法（multitaper method ,MTM法）
功率譜估計(jì)----最大熵法（maxmum entmpy method,MEM法）
功率譜估計(jì)----多信號(hào)分類法（multiple signal clasSification,music法）Fourier transform to strike a signal to the power spectrum - the cycle of plans
Fourier transform to strike a signal to the power spectrum - Sub-cycle Method
Fourier transform to strike a signal to the power spectrum --- welch method
Power spectrum estimated more than window ---- Law (multitaper method, MTM)
---- Power spectrum estimate of maximum entropy (maxmum entmpy method, MEM)
---- More than the estimated power spectrum signal clasSification (multiple signal clasSification, music)
