Chi Square Test for Independence: calculuate the chi square of a vector and the critical value.
標簽： Independence calculuate the critical
上傳時間： 2013-11-29
上傳用戶：xyipie
This functions returns the variance of vector elements
標簽： functions elements variance returns
上傳時間： 2015-09-23
上傳用戶：edisonfather
this some codes for the book <<space-time codes and mimo systerms>>
標簽： codes space-time systerms this
上傳時間： 2013-12-17
上傳用戶：aa54
it is very good codes for space-time systerms
標簽： space-time systerms codes good
上傳時間： 2014-01-22
上傳用戶：FreeSky
Basic Differential Encoding/Decoding of input vector of numeric values
標簽： Differential Decoding Encoding numeric
上傳時間： 2013-12-20
上傳用戶：qiaoyue
by Jay Kadane。Input:a vector with floats.Output:the maximum submatrix.
標簽： submatrix maximum Kadane Output
上傳時間： 2015-10-13
上傳用戶：彭玖華
用MATLAB程序實現support vector machine
標簽： support machine MATLAB vector
上傳時間： 2015-10-17
上傳用戶：qq1604324866
in this paper,wo propose an extension of the zerotree-based space-frequency quantization algorithm by adding a wedgelet symbol to its tree-pruning optimization.
標簽： space-frequency zerotree-based quantization algorithm
上傳時間： 2015-10-22
上傳用戶：yxgi5
Verilog HDL: Magnitude For a vector (a,b), the magnitude representation is the following: A common approach to implementing these arithmetic functions is to use the Coordinate Rotation Digital Computer (CORDIC) algorithm. The CORDIC algorithm calculates the trigonometric functions of sine, cosine, magnitude, and phase using an iterative process. It is made up of a series of micro-rotations of the vector by a set of predetermined constants, which are powers of two. Using binary arithmetic, this algorithm essentially replaces multipliers with shift and add operations. In a Stratix™ device, it is possible to calculate some of these arithmetic functions directly, without having to implement the CORDIC algorithm.
標簽： representation Magnitude the magnitude
上傳時間： 2013-12-24
上傳用戶：金宜
(數據挖掘新方法-支持向量機)PDF格式的!知道支持向量機(SVM),肯定知道它是一本非常經典的圖書!支持向量機(support vector machine，SVM)是數據挖掘中的一項新技術，是借助于最優化方法解決機器學習問題的新工具．
標簽： support machine vector 支持向量機
上傳時間： 2013-12-06
上傳用戶：kr770906
