20,463 research outputs found
An Open Source Pattern Recognition Toolbox for MATLAB
Pattern recognition and machine learning are becoming integral parts of
algorithms in a wide range of applications. Different algorithms and approaches
for machine learning include different tradeoffs between performance and
computation, so during algorithm development it is often necessary to explore a
variety of different approaches to a given task. A toolbox with a unified
framework across multiple pattern recognition techniques enables algorithm
developers the ability to rapidly evaluate different choices prior to
deployment. MATLAB is a widely used environment for algorithm development and
prototyping, and although several MATLAB toolboxes for pattern recognition are
currently available these are either incomplete, expensive, or restrictively
licensed. In this work we describe a MATLAB toolbox for pattern recognition and
machine learning known as the PRT (Pattern Recognition Toolbox), licensed under
the permissive MIT license. The PRT includes many popular techniques for data
preprocessing, supervised learning, clustering, regression and feature
selection, as well as a methodology for combining these components using a
simple, uniform syntax. The resulting algorithms can be evaluated using
cross-validation and a variety of scoring metrics to ensure robust performance
when the algorithm is deployed. This paper presents an overview of the PRT as
well as an example of usage on Fisher's Iris dataset
Keeping track of worm trackers
C. elegans is used extensively as a model system in the neurosciences due to its well defined nervous system. However, the seeming simplicity of this nervous system in anatomical structure and neuronal connectivity, at least compared to higher animals, underlies a rich diversity of behaviors. The usefulness of the worm in genome-wide mutagenesis or RNAi screens, where thousands of strains are assessed for phenotype, emphasizes the need for computational methods for automated parameterization of generated behaviors. In addition, behaviors can be modulated upon external cues like temperature, O2 and CO2 concentrations, mechanosensory and chemosensory inputs. Different machine vision tools have been developed to aid researchers in their efforts to inventory and characterize defined behavioral “outputs”. Here we aim at providing an overview of different worm-tracking packages or video analysis tools designed to quantify different aspects of locomotion such as the occurrence of directional changes (turns, omega bends), curvature of the sinusoidal shape (amplitude, body bend angles) and velocity (speed, backward or forward movement)
Information Theoretical Estimators Toolbox
We present ITE (information theoretical estimators) a free and open source,
multi-platform, Matlab/Octave toolbox that is capable of estimating many
different variants of entropy, mutual information, divergence, association
measures, cross quantities, and kernels on distributions. Thanks to its highly
modular design, ITE supports additionally (i) the combinations of the
estimation techniques, (ii) the easy construction and embedding of novel
information theoretical estimators, and (iii) their immediate application in
information theoretical optimization problems. ITE also includes a prototype
application in a central problem class of signal processing, independent
subspace analysis and its extensions.Comment: 5 pages; ITE toolbox: https://bitbucket.org/szzoli/ite
Review of analytical instruments for EEG analysis
Since it was first used in 1926, EEG has been one of the most useful
instruments of neuroscience. In order to start using EEG data we need not only
EEG apparatus, but also some analytical tools and skills to understand what our
data mean. This article describes several classical analytical tools and also
new one which appeared only several years ago. We hope it will be useful for
those researchers who have only started working in the field of cognitive EEG
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