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.
標(biāo)簽： handbook chapters presents overview
上傳時(shí)間： 2013-12-15
上傳用戶：yuzsu
高斯過(guò)程是一種非參數(shù)化的學(xué)習(xí)方法，它可以很自然的用于regression，也可以用于ClaSSification。本程序用高斯過(guò)程實(shí)現(xiàn)分類！
上傳時(shí)間： 2015-10-22
上傳用戶：gxf2016
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
標(biāo)簽： the provides computer download
上傳時(shí)間： 2015-11-29
上傳用戶：ywqaxiwang
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
標(biāo)簽： method statistic learning Support
上傳時(shí)間： 2014-12-02
上傳用戶：zukfu
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.
標(biāo)簽： efficient AdaBoost learning learners
上傳時(shí)間： 2013-12-06
上傳用戶：小寶愛(ài)考拉
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.
標(biāo)簽： statistical Classical Abstract pattern
上傳時(shí)間： 2013-11-25
上傳用戶：www240697738
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)
標(biāo)簽： Fourier welch 變換 信號(hào)
上傳時(shí)間： 2016-07-16
上傳用戶：wqxstar
Some paintings lyapunov chaotic attractor and the related terms, while the sub-critical and supercritical ClaSSification
標(biāo)簽： sub-critical paintings attractor and
上傳時(shí)間： 2016-07-29
上傳用戶：龍飛艇
決策樹(shù)分類，Your task for this project is to develop an system which performs a ClaSSification task with any various Decision Tree.
上傳時(shí)間： 2016-08-01
上傳用戶：源弋弋
統(tǒng)計(jì)模式識(shí)別、線性或非線性回歸以及人工神經(jīng)網(wǎng)絡(luò)等方法是數(shù)據(jù)挖掘的有效工具，支持向量分類（support vector ClaSSification，簡(jiǎn)稱SVC）算法是一個(gè)很有發(fā)展前景的方向。
標(biāo)簽： 模式識(shí)別 回歸 人工神經(jīng)網(wǎng)絡(luò) 數(shù)據(jù)挖掘
上傳時(shí)間： 2014-02-16
上傳用戶：lifangyuan12
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