Search Results for "matlab classification learner"

An alignment-free standalone tool with interactive graphical user interface for DNA sequence comparison and analysis

Advanced detection, classification and localization (DCL) of marine mammals and passive acoustic monitoring (PAM). Code is being developed as Matlab routines, interfaces for Ishmael (http://www.bioacoustics.us/ishmael.html), and in other formats.

All future developments will be implemented in the new MATLAB toolbox SciXMiner, please visit https://sourceforge.net/projects/scixminer/ to download the newest version. The former Matlab toolbox Gait-CAD was designed for the visualization and analysis of time series and features with a special focus to data mining problems including classification, regression, and clustering.

MORphological PHenotype Extraction (MORPHE) is a suite of automated image processing, visualization, and classification algorithms to facilitate the analysis of heritable and clonal red-to-green transitions that occurred during the growth of a colony.

CS-Miner stands for Chemical Space Miner and is a software tool for navigating in chemical space of compound databases. It helps for deriving appropriate classification models and performing virtual screening. Download it via: http://csminer.com/csm/?p=7 A quick tutorial is available through: http://csminer.com/csm/?p=8

A feature selection and classification algorithm. It is based on L1 Norm Support Vector Machine with t-test and Recursive Feature Elimination.

CRUMp is based on a kernel-based learning method called Classification Relevance Units Machine (CRUM). Given an input set of protein sequences in FASTA format, the system outputs the position, residue type (S, T, or Y), and the estimated probability of each tested site being phosphorylatable. Latest downloadable files: - crump-0.2.0.tar.gz: CRUMp GNU Octave package - crump-0.2.0.zip: CRUMp MATLAB script - crumptestset.fasta: A testing dataset in FASTA format.

This is a c-library that provides tools for advanced analysis of electrophysiological data. It features denoising, unsupervised classification, time-frequency analysis, phase-space analysis, neural networks, time-warping and more.

Implematation of robust depth-based inference tools for microarray data (a scale curve, to measure the dispersion of a set of curves, a rank test to decide if two groups of curves come from the same population, and classification techniques).