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Mostrando ítems 1-10 de 16
Classification Rule Mining with Iterated Greedy
(2017-03-30)
In the context of data mining, classi cation rule discovering
is the task of designing accurate rule based systems that model the useful
knowledge that di erentiate some data classes from others, and is present
in large ...
A Classification Module for Genetic Programming Algorithms in JCLEC
(MIT Press, 2014)
JCLEC-Classi cation is a usable and extensible open source library for genetic program-
ming classi cation algorithms. It houses implementations of rule-based methods for clas-
si cation based on genetic programming, ...
Multi-Objective Genetic Programming for Feature Extraction and Data Visualization
(2017-03-31)
Feature extraction transforms high dimensional
data into a new subspace of lower dimensionalitywhile keeping
the classification accuracy. Traditional algorithms do not
consider the multi-objective nature of this task. ...
JCLEC Meets WEKA!
(2014-02-27)
WEKA has recently become a very referenced DM tool. In
spite of all the functionality it provides, it does not include any framework
for the development of evolutionary algorithms. An evolutionary
computation framework ...
Parallelization Strategies for Markerless Human Motion Capture
(2015-10-15)
Markerless Motion Capture (MMOCAP) is the
problem of determining the pose of a person from images
captured by one or several cameras simultaneously without
using markers on the subject. Evaluation of the solutions
is ...
Speeding up Multiple Instance Learning Classification Rules on GPUs
(2017-01-19)
Multiple instance learning is a challenging task in supervised learning and data mining. How-
ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets.
Graphics processing ...
Cifrado de imágenes y Matemáticas
(Universidad Nacional de La Plata, 2011)
Un tema que debe interesar al profesorado de Matemáticas de todos los niveles educativos es cómo hacer comprender a nuestros alumnos la utilidad de los conceptos matemáticos que están estudiando en nuestras asignaturas. ...
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
(2015-10-15)
Supervised discretization is one of basic data preprocessing
techniques used in data mining. CAIM (Class-
Attribute InterdependenceMaximization) is a discretization
algorithm of data for which the classes are known. ...
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
(2017-01-19)
Individuals from Pittsburgh rule-based classifiers represent a complete solution
to the classification problem and each individual is a variable-length set
of rules. Therefore, these systems usually demand a high level ...
Speeding Up Evolutionary Learning Algorithms using GPUs
(ESTYLF, 2010)
This paper propose a multithreaded Genetic
Programming classi cation evaluation model
using NVIDIA CUDA GPUs to reduce the
computational time due to the poor perfor-
mance in large problems. Two di erent clas-
si ...