wkiro
所属分类:hotest
开发工具:matlab
文件大小:0KB
下载次数:1
上传日期:2013-09-04 06:25:55
上 传 者:
sh-1993
说明: 一种具有核密度估计和带宽选择的无参数经验贝叶斯分类器的实现。
(An implementation of a parameterless empiric Bayesian classifier with kernel density estimation and bandwidth selection.)
文件列表:
js/bandwidth selection/
matlab/
wkiro
=====
An implementation of a parameterless empiric Bayesian classifier with a kernel estimator and bandwidth selection.
Fully implemented in Matlab, partially in JS.
Usage instructions
---
`main.m` is the application entry point. The simplest way to run the whole thing is to just type `main` in Matlab's
Command Window.
The application execution can be parameterized by the following variables:
- `TRAINING_SET_PARAMETERS`, specifying a matrix of parameter vectors for each training dataset.
- Generated sets can be adjusted in the following dimensions:
- X,Y coordinates of the training set center,
- radius of the training set.
- The number of parameter vectors in the matrix also specifies the number of classes, to which the target elements
will be ascribed.
- One parameter vector describes the features of one training set.
- The data format for the training set parameter matrix is the following:
```matlab
[x1,y1,r1; x2,y2,r2; x3,y3,r3] % unrestricted matrix length
```
- In case no value is defined for the variable, the following default is used:
```matlab
[-15,-15,1; 15,15,1; -15,15,1; 15,-15,1];
```
- `TRAINING_SET_ELEMENTS_COUNT`, specifying the quantity of elements forming each training set.
- Every training set has an equal number of elements.
- In case no value is defined for the variable, the following default is used:
```matlab
20
```
- `ELEMENTS`, specifying the set of elements to classify.
- An element is represented by a pair of coordinates.
- The format for the data within this variable is as follows:
```matlab
[x1,y1; x2,y2; x3,y3] % unrestricted matrix length
```
- In case no value is defined for the variable, a set of 100 elements generated randomly within a circular area cenetered at `(0,0)` and with a radius of `7` is used as the default.
- `KERNEL`, specifying the function used for kernel estimation by the classifier.
- The variable is meant to be a function handle and can be used only as such.
- There are a few predefined kernel functions available in the `kernels` directory for you to use.
- In case no value is defined for the variable, the Gaussian kernel is used as the default.
todo
---
- update the implementation in JS
- add unit test coverage in JS
- fix the bug under Octave.
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