Search Results for "discriminant analysis"
Sort By:
Showing 7 open source projects for "discriminant analysis"
View related business solutions-
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
1
Vascular prediction
Study develops predictive model to reduce 90-day readmissions.
...A study developed a predictive model for 90-day hospital readmission in patients undergoing elective vascular procedures. The best predictive model was Shrinkage Discriminant Analysis, which considered variables such as length of stay, comorbidity scores, procedure type, and admission type. The model indicates that efforts to reduce vascular readmissions should prioritize emergency procedures. This risk stratification allows for better identification and prevention of unnecessary readmissions, crucial in an environment where preventing unplanned readmissions is increasingly important. -
2
Fuzzy Ecospace Modelling
FEM allows users to create fuzzy functional groups for use in ecology.
...FEM clusters n-dimensional matrices of functional traits (ecospace matrices – here called the Training Matrix) into functional groups and converts them into fuzzy functional groups using fuzzy discriminant analysis (Lin and Chen 2004 – see main text for more information). Following this, FEM classifies the functional entities from a second matrix (the Test Matrix) into the groups made using the Training Matrix, generating fuzzy membership values for each unit in the Test Matrix. These values are real numbers from 0 to 1, representing increasing degrees of “truth” regarding an organism’s membership in the fuzzy set (see main text). ... -
3
RDA Toolbox
MATLAB tool for analysis of Raman spectra.
Provides principal component analysis, discriminant analysis, peak analysis, and clustering tools powered by MATLAB in an easy-to-use interface for the interpretation of Raman spectroscopy data. -
4
popanc
estimate population ancestry frequencies in admixed populations
This program uses discriminant analysis and Markov chain Monte Carlo to infer local ancestry frequencies in an admixed population from genomic data. This is done in the context of a continuous correlated beta process model that accounts for expected autocorrelations in local ancestry frequencies along chromosomes. This method is best-suited for the analysis of admixed populations that experience little or no on-going gene flow from the parental populations, but that are still segregating for ancestry segments. -
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
5
Online Signature Verification
Real time signature verification using MATLAB and C#
...The signature is characterized as pen-strokes consisting x-y coordinates and pressure with the data being stored in a signature database. Dynamic time warping (DTW) and quadratic discriminant analysis (QDA) is used to get results where the system accepts 2% of forged signatures and rejects 5% of true signatures -
6
dAnalytics
dAnalytics is a software made for education in discriminant analysis.
dAnalytics is a software made for educational purposes to work with discriminant analysis. It's a very simple calculator over bi-dimensional populations. You input a file with two columns (one for each variable) and tryout different separations of populations (blue and red) and analyze it's efficiency by observing the eigen value result for the attempted separation. It also gives many information about the statistics both of the input and the separated populations. ... -
7
AGNES Cluster identification
R script to run several clustering algorithms using morphometric data
The R script was written to perform and compare several versions of cluster algorithms as described in: Seifert, Ritz & Csősz (2013) published in Myrmecological News. The aim is to identify (cryptic) taxa based on numeric data on morphology. The first step is a Linear Discriminant Analysis (LDA) based on subunits (ant nests) independent of any taxon information. Following analysis are Non-Metric Multidimensional Scaling (NMDS) and k-Means clustering. The NMDS scores are also summarized using K-Means. For comparison, k-Means is also run on the nest means (without preceeding LDA). Cluster assignment is summarized in an output file for all three methods.
- Previous
- You're on page 1
- Next
