最新的支持向量機工具箱，有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
上傳時間： 2013-12-16
上傳用戶：亞亞娟娟123
JAVA開發的日期控件，帶星期的，可以錄入又可以自動選擇，內附手冊及示例，支持time型。
上傳時間： 2013-12-26
上傳用戶：朗朗乾坤
介紹幾種cpuThe 8xC251SA/SB/SP/SQ improves on the MCS-51 architecture and peripheral features, introducing the advanced register based CPU architecture i.e., the MCS 251 microcontroller architecture. The register based CPU supports a 40-byte register file. In addition, the 8xC251SA/SB/SP/SQ microcontroller has 256-Kbyte expanded external code/data memory space and 64-Kbyte stack space. The new controller is also specially designed to execute C code efficiently. More importantly, the 8xC251SA/SB/SP/SQ maintains binary code compatibility with MCS 51 microcontrollers but at the same time allows the use of the powerful MCS 251 microcontroller instruction set, with many new 8, 16 and 32 bit instructions available. The 8xC251SA/SB/SP/SQ has 512 bytes or 1 Kbyte of on-chip data RAM options and is available in 16 Kbytes and 8 Kbytes of on-chip ROM/OTPROM or ROMless options.
標簽： architecture introducin peripheral improves
上傳時間： 2015-03-15
上傳用戶：ccclll
FreeRTOS，這個比 Real Time Linux 和 eCos 更小的實時操作系統，并且你也不用為這兩個大的操作系統弄得頭大了。FreeRTOS是一個非常小的實時操作系統，它包含有一個實時的內核及一個內核調制程序。它支持的單片機有 Philips ARM7, TI MSP430, Renesas (Hitachi) H8/S, Atmel AVR, Motorola/Freescale HCS12, Motorola/Freescale ColdFire, 及其他8051的單片機
標簽： FreeRTOS
上傳時間： 2014-05-28
上傳用戶：253189838
Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem
標簽： polynomial regression weighted instance
上傳時間： 2013-11-28
上傳用戶：sunjet
This directory contains utility for implementing generic Reqursive Least Squares (RLS) algorithm. The example shows how one can use the utility to estamate the parameters of a simple linear discrete time system.
標簽： implementing Reqursive directory algorithm
上傳時間： 2014-01-06
上傳用戶：gtf1207
Unix 第 6 版的 sh 手冊和源碼，感興趣請參考`The UNIX Time-Sharing System , CACM, July, 1974，它給出 Shell 操作的理論。 chdir (I), login (I), wait (I), shift (I)
上傳時間： 2014-08-15
上傳用戶：wys0120
Overview In this chapter I introduce Borland C++Builder (BCB) and explain what it is about. I also devote considerable time to explaining the purpose of this book and the philosophy behind my approach to technical writing. Technical subjects covered in this chapter include Creating a simple Multimedia RAD program that plays movies, WAV files, and MIDI files. Shutting down the BCB RAD programming tools and writing raw Windows API code instead. Creating components dynamically on the heap at runtime. Setting up event handlers (closures) dynamically at runtime. A brief introduction to using exceptions. This topic is covered in more depth in Chapter 5, "Exceptions." A brief introduction to ANSI strings. This subject is covered in more depth in Chapter 3, "C++Builder and the VCL." Using the online help. Greping through the include and source files that come with the product and with this book.
標簽： introduce Overview Borland Builder
上傳時間： 2014-01-04
上傳用戶：小鵬
系統下,地球坐標轉換的源代碼,在WGS-84坐標和北京54坐標之間的一套轉換參數,可以全國通用的，在每個地方會不一樣，因為它們是兩個不同的橢球基準。 // WGS2GK.CPP : C++ version of Ottmar Labonde s WGSDHDN3.PAS with CPU time measurement // compile with MS Visual C++ version 6.0 or do the necessary modifications for your compiler // // Conversion of
上傳時間： 2014-01-09
上傳用戶：hxy200501
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
標簽： instantaneous algorithm Bayesian Gaussian
上傳時間： 2013-12-19
上傳用戶：jjj0202
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