这是表达支持向量机一个demo，可以帮助更好的理解svm
上传时间： 2013-11-27
上传用户：libenshu01
支持向量机工具箱使用方法演示，讲述如何使用svm的工具箱。
上传时间： 2017-06-18
上传用户：WMC_geophy
跟類神經網路有點像的東西， 不過現今最常拿來就是做分類也就是說，如果我有一堆已經分好類的東西 （可是分類的依據是未知的！） ，那當收到新的東西時， SVM 可以預測 (predict) 新的資料要分到哪一堆去。
标签：
上传时间： 2014-01-18
上传用户：hasan2015
The document explains the concept of multiple-bandpass filtering and compacting of arbitrarily spaced frequency bands.
标签： multiple-bandpass arbitrarily compacting filtering
上传时间： 2017-07-03
上传用户：pinksun9
支持向量机导论中文版，学习SVM的好材料
标签： 支持向量机
上传时间： 2014-01-17
上传用户：caixiaoxu26
this manual for pc3000 mrtool! special repair harddisk for maxtor!but i need software pc3000 mrtool!you won look site : http://space.doit.com.cn/viewthread-45664.html
上传时间： 2017-07-23
上传用户：talenthn
matlab图像处理工具相，使用了主成分分析，ANN，SVM等方法。
上传时间： 2017-07-23
上传用户：hphh
Abstract—We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space.
标签： technique operators Abstract describe
上传时间： 2014-01-23
上传用户：ruixue198909
Implementation of Edmonds Karp algorithm that calculates maxFlow of graph. Input: For each test case, the first line contains the number of vertices (n) and the number of arcs (m). Then, there exist m lines, one for each arc (source vertex, ending vertex and arc weight, separated by a space). The nodes are numbered from 1 to n. The node 1 and node n should be in different sets. There are no more than 30 arcs and 15 nodes. The arc weights vary between 1 and 1 000 000. Output: The output is a single line for each case, with the corresponding minimum size cut. Example: Input: 7 11 1 2 3 1 4 3 2 3 4 3 1 3 3 4 1 3 5 2 4 6 6 4 5 2 5 2 1 5 7 1 6 7 9 Output: 5
标签： Implementation calculates algorithm Edmonds
上传时间： 2014-01-04
上传用户：kiklkook
Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and Kennedy (Ebarhart, Kennedy, 1995 Kennedy, Eberhart, 1995 Ebarhart, Kennedy, 2001). The PSO is a population based search algorithm based on the simulation of the social behavior of birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the graceful and unpredictable choreography of a bird folk. Each individual within the swarm is represented by a vector in multidimensional search space.
标签： optimization introduced originally and
上传时间： 2017-09-08
上传用户：hoperingcong
