function y_cum = cum2x (x,y, maxlag, nsamp, overlap, flag) ％CUM2X Cross-covariance ％ y_cum = cum2x (x,y,maxlag, samp_seg, overlap, flag) ％ x,y - data vectors/matrices with identical dimensions ％ if x,y are matrices, rather than vectors, columns are ％ assumed to correspond to independent realizations, ％ overlap is set to 0, and samp_seg to the row dimension. ％ maxlag - maximum lag to be computed [default = 0] ％ samp_seg - samples per segment [default = data_length] ％ overlap - percentage overlap of segments [default = 0] ％ overlap is clipped to the allowed range of [0,99].
標簽： cum2x y_cum Cross-covariance function
上傳時間： 2015-09-08
上傳用戶：xieguodong1234
a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.
標簽： Classifiers Implemented Bayesian applying
上傳時間： 2015-09-11
上傳用戶：ommshaggar
Verilog and VHDL狀態機設計，英文pdf格式 State machine design techniques for Verilog and VHDL Abstract : Designing a synchronous finite state Another way of organizing a state machine (FSM) is a common task for a digital logic only one logic block as shown in engineer. This paper will discuss a variety of issues regarding FSM design using Synopsys Design Compiler . Verilog and VHDL coding styles will be 2.0 Basic HDL coding presented. Different methodologies will be compared using real-world examples.
上傳時間： 2013-12-19
上傳用戶：change0329
In this paper, we describe the development of a mobile butterfly-watching learning (BWL) system to realize outdoor independent learning for mobile learners. The mobile butterfly-watching learning system was designed in a wireless mobile ad-hoc learning environment. This is first result to provide a cognitive tool with supporting the independent learning by applying PDA with wireless communication technology to extend learning outside of the classroom. Independent learning consists of self-selection, self-determination, self-modification, and self-checking.
標簽： butterfly-watching development describe learning
上傳時間： 2014-11-26
上傳用戶：waizhang
來自澳大利亞Qeensland大學的計算機視覺Matlab工具箱。 This Toolbox provides a number of functions that are useful in computer vision, machine vision and related areas. It is a somewhat eclectic collection reflecting the author s personal interest in areas of photometry, photogrammetry, colorimetry. It covers functions such as image file reading and writing, filtering, segmentation, feature extraction, camera calibration, camera exterior orientation, display, color space conversion and blackbody radiators. The Toolbox, combined with MATLAB and a modern workstation computer, is a useful and convenient environment for investigation of machine vision algorithms. It is possible to use MEX files to interface with image acquisition hardware ranging from simple framegrabbers to Datacube servers.
標簽： Qeensland functions provides Toolbox
上傳時間： 2015-09-30
上傳用戶：qb1993225
基于libsvm，開發的支持向量機圖形界面(初級水平)應用程序，并提供了關于C和sigma的新的參數選擇方法，使得SVM的使用更加簡單直觀.參考文章 Fast and Efficient Strategies for Model Selection of Gaussian Support Vector Machine 可google之。
標簽： libsvm
上傳時間： 2015-10-16
上傳用戶：cuibaigao
A small tool to change the default source control provider client, so you can use SourceSafe, SourceOffsite, Workspaces etc, on the same machine
標簽： SourceSafe provider default control
上傳時間： 2015-10-17
上傳用戶：ggwz258
acm HDOJ 1051WoodenSticks Description: There is a pile of n wooden sticks. The length and weight of each stick are known in advance. The sticks are to be processed by a woodworking machine in one by one fashion. It needs some time, called setup time, for the machine to prepare processing a stick. The setup times are associated with cleaning operations and changing tools and shapes in the machine. The setup times of the woodworking machine are given as follows: (a) The setup time for the first wooden stick is 1 minute. (b) Right after processing a stick of length l and weight w , the machine will need no setup time for a stick of length l and weight w if l<=l and w<=w . Otherwise, it will need 1 minute for setup.
標簽： WoodenSticks Description length wooden
上傳時間： 2014-03-08
上傳用戶：netwolf
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
標簽： Introduction Classifiers Algorithms introduces
上傳時間： 2015-10-20
上傳用戶：aeiouetla
This book is the most accurate and up-to-date source of information the STL currently available. ... It has an approach and appeal of its own: it explains techniques for building data structures and algorithms on top of the STL, and in this way appreciates the STL for what it is - a framework. Angelika Langer, Independent Consultant and C++ Report Columnist "A superbly authored treatment of the STL......an excellent book which belongs in any serious C++ developer s library." Jim Armstrong, President 2112 F/X, Texas. \n The C++ Standard Template Library (STL) represents a breakthrough in C++ programming techniques. With it, software developers can achieve vast improvements in the reliability of their software, and increase their own productivity.
標簽： information up-to-date available currently
上傳時間： 2015-10-31
上傳用戶：CHINA526
