Search Results for "matlab classification learner"

Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score.

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

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.

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).

The Databionics ESOM Tools offer many data mining tasks using Emergent Self-Organizing Maps. Visualization, clustering, and classification of high-dimensional data using databionics principles can be performed interactively or automatically.