Search
Code Directory
 ASP
 ASP.NET
 C/C++
 CFML
 CGI/PERL
 Delphi
 Development
 Flash
 HTML
 Java
 JavaScript
 Pascal
 PHP
 Python
 SQL
 Tools
 Visual Basic & VB.NET
 XML
New Code
.Net Runtime Library for Delphi 6.0.4.0
Scimbo 1.64
AnyMap JS Maps 8.4.2
GetOrgChart 2.5.3
AnyChart JS Charts and Dashboards 8.4.2
OrgChart JS 3.8.0
dbForge Compare Bundle for MySQL 8.1
dbForge Search for SQL Server 2.3
Database Workbench Pro 5.5.0
Luxand FaceSDK 7.0
SSIS Data Flow Components 1.10
Entity Developer Professional 6.3
dbForge Index Manager for SQL Server 1.9
dbForge Data Generator For MySQL 2.2
Magento Australia Post eParcel Extension 1.0
Top Code
PHP Point of sale 10.0
MATLAB Support Package for Arduino (aka ArduinoIO Package) 1.0
Betting system 6.x-1.x-dev
Java-2-Pseudo 1.0
Faculty Evaluation System 1.1
TeeBI for RAD Studio Suite 2017
Cuckoo Search (CS) Algorithm 1.0
Student Information Management System 1.0
Java/RTR 1.0
JEDI Database Desktop 27012002
Mind Fighter 1.1
JAC (Java Asn.1 Compiler) 3.0
000-516 Free Test Exam Questions 10.0
ICDoctorAppointment - Doctor Appointment Script 1.2
CONTRAST CONTROLLER 1.0
Top Rated
Deals and Discounts Website Script 1.0.2
ADO.NET Provider for ExactTarget 1.0
Solid File System OS edition 5.1
Classified Ad Lister 1.0
Aglowsoft SQL Query Tools 8.2
ICPennyBid Penny Auction Script 4.0
PHP Review Script 1.0
ATN Resume Finder 2.0
ATN Site Builder 3.0
Availability Booking Calendar PHP 1.0
PHP GZ Blog Script 1.1
ATN Jobs Software 4.0
ATN Mall 2.0
WeBuilder 2015 13.3
PHP Digital Download Script 1.0.4
Discriminant Analysis Programme 1.0
File ID: 82875






Discriminant Analysis Programme 1.0
Download Discriminant Analysis Programme 1.0http://www.mathworks.comReport Error Link
License: Shareware
File Size: 143.4 KB
Downloads: 20
Submit Rating:
Discriminant Analysis Programme 1.0 Description
Description: The purpose of Discriminant Analysis Programme (DAP) is to facilitate discrimination and classification of (to be) grouped data with robust estimation- and modeled structures for the covariances in a one-go software. The robust estimation methods are the S-estimator and Donoho-Stahel estimator. The included covariance structure models are Common Principal Components, Proportional, classical Quadratic and Linear ones; Hypothesis Testing is performed for these fitted models except for arbitrary covariances. The Discriminant Rules are found and the Classification Rules Coefficients are computed after the given training data sample and used to classify the classification sample data, if provided. They are also used to find malclassified data elements by Cross-Validation (Leave-One-Out) method of the training sample, being recomputed for each element.
It includes complementary graphical outputs for bivariate data such as normality plots and group separation.

License: Shareware

Related: Training, Sample, classify, malclassified, provided, computed, coefficients

O/S:BSD, Linux, Solaris, Mac OS X

File Size: 143.4 KB

Downloads: 20



More Similar Code

Leave-one-out cross-validation for PLS regression or discriminant analysis

pls_cv = plscv(x,y,vl,'da')

input:
x (samples x descriptors) for cross-validation
y (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0.
vl (1 x 1) number of latent variables to compute in cross-validation
'da' (char) to indicate PLS-discriminant analysis (in PLS...



Leave-one-out cross-validation for PLS regression or discriminant analysis

pls_cv = plscv(x,y,vl,'da')

input:
x (samples x descriptors) for cross-validation
y (samples x variables) for regression or (samples x...



Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation called canonical discriminant analysis. It derives the canonical coefficients parallels that of one-way MANOVA and...



Discriminant Analysis is a multivariate technique concerned with separating distinct sets of observations to previously defined groups; rather exploratory in nature, it is a separatory procedure. Here, we develop this technique considering normal...



This is the fast implementation of Null LDA method. [W,CPU_TIME]=FNLDA(Data,ClassLabel)
Null linear discriminant analysis (LDA) method is a popular dimensionality reduction method for solving small sample size problem. The implementation of...



Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated.



These are the codes in "A note on two-dimensional linear discrimant analysis", Pattern Recognition Letter'
In this paper, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA...



1 Introduction

PLS-DA for data analysis in chemistry and OMICS studies. Also included in this package are 3 variable selection methods:
1) target projection (TP)
2) competitive adaptive reweighted sampling (CARS)
3)...



Refer to the following papers for a description. Also note that you can play around with the parameters to optimize the performance, if it is not good enough on your dataset, thought it should be.

[1] R. N. Khushaba, S. Kodagoa, Dikai...



Features of this implementation of LDA:
- Allows for >2 classes
- Permits user-specified prior probabilities
- Requires only base MATLAB (no toolboxes needed)
- Assumes that the data is complete (no missing values)
-...

User Review for Discriminant Analysis Programme
- required fields
     

Please enter text on the image