How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the Training samples randomly, or systematically as the following:
Take the first k Training sample as single-element clusters
Assign each of the remaining (N-k) Training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the Training sample causes no new assignments.
The market for miniature computer programming is exploding. C++ Footprint and Performance Optimization supplies programmers the knowledge they need to write code for the increasing number of hand-held devices, wearable computers, and intelligent appliances.
This book gives readers valuable knowledge and programming techniques that are not currently part of traditional programming Training.
In the world of C++ programming, all other things being equal, programs that are smaller and faster are better.
C++ Footprint and Performance Optimization contains case studies and sample code to give readers concrete examples and proven solutions to problems that don t have cut and paste solutions.
The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2.
The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a Training set of N (single) output-(multi) input samples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂，據高手說，非常有用。
C++ Footprint and Performance Optimization contains case studies and sample code to give readers concrete examples and proven solutions to problems that don t have cut and paste solutions. The market for miniature computer programming is exploding. C++ Footprint and Performance Optimization supplies programmers the knowledge they need to write code for the increasing number of hand-held devices, wearable computers, and intelligent appliances. This book gives readers valuable knowledge and programming techniques that are not currently part of traditional programming Training.
This code in this directory implements the binary hopfield network.Source code may be found in HOPNET.CPP. A sample Training file is H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST,H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the
screen and a file, ARCHIVE.LST.
This book is designed to teach you the best practices in developing Windows DNA applications.
We have avoided making this book a primer on every technology associated with
Windows DNA. If we had followed this course, this would be an encyclopedia set.
Everyone has their favorite authors and books on the various technical subject areas. The
market is full of books to teach you the basics, the how, this book tries to be different in
that we pull out the important points to teach you about the why. If you need Training in
a particular technology covered in this book, Sams has a number of 24-hour and 21-day
books that cover a wide range of topics.
* Lightweight backpropagation neural network.
* This a lightweight library implementating a neural network for use
* in C and C++ programs. It is intended for use in applications that
* just happen to need a simply neural network and do not want to use
* needlessly complex neural network libraries. It features multilayer
* feedforward perceptron neural networks, sigmoidal activation function
* with bias, backpropagation Training with settable learning rate and
* momentum, and backpropagation Training in batches.
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