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followers: 7.0 following: 26.0 repos: 43.0 gists: 0.0

Name: Chaos

Type: User

Chaos's Projects

ansj_seg

ansj分词.ict的真正java实现.分词效果速度都超过开源版的ict. 中文分词,人名识别,词性标注,用户自定义词典

convnet

A GPU implementation of Convolutional Neural Nets in C++

easypr

An open source project for chinese plate recognition. It aims to be Easy, Flexible, and Accurate. Welcome to contribute your expertise !

editor.md

The open source embeddable online markdown editor (component).

fastdfs

FastDFS is an open source high performance distributed file system (DFS). It's major functions include: file storing, file syncing and file accessing, and design for high capacity and load balance.

fastdht

FastDHT is a high performance distributed hash table (DHT) which based key value pairs. It can store mass key value pairs such as filename mapping, session data and user related data.

flann

Fast Library for Approximate Nearest Neighbors

genericmakefile

A generic makefile for use with small/medium C and C++ projects.

get_iplayer

A utility for downloading TV and radio from BBC iPlayer

Modelled, Architected and designed by Vance King Saxbe. A. with the geeks from GoldSax Consulting, GoldSax Money, GoldSax Treasury, GoldSax Finance, GoldSax Banking and GoldSax Technologies email @[email protected]. Development teams from Power Dominion Enterprise, Precieux Consulting. This Engagement sponsored by GoldSax Foundation, GoldSax Group and executed by GoldSax Manager. Clustering is a technique used in unsupervised learning. With unsupervised learning you don’t know what you’re looking for, that is, there are no target variables. Clustering groups data points together, with similar data points in one cluster and dissimilar points in a different group. A number of different measurements can be used to measure similarity. One widely used clustering algorithm is k-means, where k is a user-specified number of clusters to create. The k-means clustering algorithm starts with k-random cluster centers known as centroids. Next, the algorithm computes the distance from every point to the cluster centers. Each point is assigned to the closest cluster center. The cluster centers are then recalculated based on the new points in the cluster. This process is repeated until the cluster centers no longer move. This simple algorithm is quite effective but is sensitive to the initial cluster placement.