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

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.

GoIFISH has been developed for the analysis of IFISH (Immunofluorescence + Fluorescence in situ Hybridisation) images, performing nuclear, membrane and spot detection. Users can manually edit segmentation results, perform background adjustments, construct heatmaps, topology maps, and perform cell classification. All results can be exported for further analysis. GoIFISH has been developed in MATLAB, however binaries are provided to run the program outside of the MATLAB environment. Source code is also available for download. To cite this software: "GoIFISH: A system for the quantification of single cell heterogeneity from IFISH images" Trinh A, Rye IH, Almendro V, Helland A, Russness HG, Markowetz F May 2014

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.

SHMTools is set of compatible software tools (mostly Matlab based) for structural health monitoring (SHM) research. This includes algorithms for system design, modeling, data acquisition, feature extraction, classification, and prognosis.

The files contained in this distribution implement a computer vision system for the classification and interpretation of flag semaphore signals. Optionally, the message can be used to send and receive TCP/IP packets using the RFC 4824 protocol.

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.

nBoost is a suite of boosting algorithms designed to solve binary classification problems on data that is not linearly separable by a convex combination of base hypotheses, i.e. noisy data. WARNING: Active development. Underlying algorithm is unstable.