We have a group of N items (represented by integers from 1 to N), and we know that there is some total order defined for these items. You may assume that no two elements will be equal (for all a, b: a<b or b<a). However, it is expensive to compare two items. Your task is to make a number of comparisons, and then output the sorted order. The cost of determining if a < b is given by the bth integer of element a of costs (space delimited), which is the same as the ath integer of element b. Naturally, you will be judged on the total cost of the comparisons you make before outputting the sorted order. If your order is incorrect, you will receive a 0. Otherwise, your score will be opt/cost, where opt is the best cost anyone has achieved and cost is the total cost of the comparisons you make (so your score for a test case will be between 0 and 1). Your score for the problem will simply be the sum of your scores for the individual test cases.
标签： represented integers group items
上传时间： 2016-01-17
上传用户：jeffery
对应论文写的时空码的仿真程序。为2天线，BPSK调制模式。自己写的Space time code simulation提供给大家
上传时间： 2014-01-27
上传用户：Shaikh
OpenSVM was developped under Visual C++ 6.0 SP6, You can open the workspace file(*.dsw) in the opensvm-src folder. The folder include the svm.h and svm.cpp which in the libsvm (Copyright (c) 2000-2007 Chih-Chung Chang and Chih-Jen Lin All rights reserved) in the opensvm-src\libsvm. However, the files svm.h and svm.cpp codes were copied/merged into stdafx.h and stdafx.cpp in order to support the development, and OpenSVM still use other codes of libsvm. So you can see the libsvm package in the source package. You can open and build it with Visual C++ 6.0. Note: the problems must be in LIBSVM format. OpenSVM project page: http://sourceforge.net/projects/opensvm If you had any question, please mail to: cloudbyron@gmail.com
标签： developped the workspace OpenSVM
上传时间： 2016-01-30
上传用户：asdfasdfd
支持向量机导轮，让你对svm有更进一步的理解，
标签： 支持向量机
上传时间： 2016-02-04
上传用户：lhw888
本文的题目是改进的核函数算法及其在人脸识别中的应用研究。 本文在系统学习现有核函数及支持向量机相关理论的基础上，系统研究了自适应选择核函数算法，通过引入朴素正则风险最小化准则，提出了一种改进的在线核函数算法。算法采用截断误差最小化、合理选取拉格郎日因子等方法对新增样本进行训练，有效地克服了现有方法收敛精度低和不能自适应选择样本的困难。 根据独立分量分析的原理和特点，将改进的核函数算法引入人脸识别的研究中，给出了基于ICA-SVM的人脸识别算法及实现方法。 论文分别应用数值仿真及现有人脸数据库，分析了算法的数值特性并验证了算法的可靠性和实用性。 本文数值仿真与分析软件基于MATLAB和LABVIEW虚拟仪器设计开发。 本文档是nh文件，可以用caj打开。与大家共享！！
上传时间： 2016-02-14
上传用户：Divine
μC/OS-II Goals Probably the most important goal of μC/OS-II was to make it backward compatible with μC/OS (at least from an application’s standpoint). A μC/OS port might need to be modified to work with μC/OS-II but at least, the application code should require only minor changes (if any). Also, because μC/OS-II is based on the same core as μC/OS, it is just as reliable. I added conditional compilation to allow you to further reduce the amount of RAM (i.e. data space) needed by μC/OS-II. This is especially useful when you have resource limited products. I also added the feature described in the previous section and cleaned up the code. Where the book is concerned, I wanted to clarify some of the concepts described in the first edition and provide additional explanations about how μC/OS-II works. I had numerous requests about doing a chapter on how to port μC/OS and thus, such a chapter has been included in this book for μC/OS-II.
标签： OS-II compatible important Probably
上传时间： 2013-12-02
上传用户：jkhjkh1982
本程序用于对训练样本提取独立主元，作为样本特征，并送入SVM分类器中训练图像的预处理中不取对数，也无须做幅度归一，由ICA的应用条件决定的。预处理后的图像以向量的形式按行排列
上传时间： 2016-02-20
上传用户：franktu
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length. It is used for reconstruction of state space in chaotic time series, and also how to determine time window.
标签： State_space_reconstruction_parame ters_in_the_analysis_of_chaotic_t the_role_of_
上传时间： 2013-12-21
上传用户：fandeshun
Nonlinear_dynamics_delay_times_and_embedding_windows. How to determine embedded window for chaotic state space of time series
标签： Nonlinear_dynamics_delay_times_an d_embedding_windows determine embedded
上传时间： 2016-02-21
上传用户：tianyi223
嵌入weka中使用的支持向量机工具包，实现SVM的分类算法，
上传时间： 2014-12-22
上传用户：开怀常笑
