This manual is licensed under the GNU General Public License version 2. More information about th... more This manual is licensed under the GNU General Public License version 2. More information about this license can be found at
This manual is licensed under the GNU General Public License version 2. More information about th... more This manual is licensed under the GNU General Public License version 2. More information about this license can be found at
Multi-label classification has rapidly attracted interest in the machine learning literature, and... more Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.
ADAMS is a modular open-source Java framework for developing workflows available for academic res... more ADAMS is a modular open-source Java framework for developing workflows available for academic research as well as commercial applications. It integrates data mining applications, like MOA, WEKA, MEKA and R, image and video processing and feature generation capabilities, spreadsheet and database access, visualizations, GIS, webservices and fast protoyping of new functionality using scripting languages (Groovy/Jython).
Data mining is said to be a field that encourages data to speak for itself rather than “forcing” ... more Data mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for aut...
An Introduction to the WEKA Data Mining System Zdravko Markov Central an extended period of time ... more An Introduction to the WEKA Data Mining System Zdravko Markov Central an extended period of time (the first version of Weka was released 11 years In sum, the Weka team has made an outstanding contribution to the data mining field. 6. s10=0, s11=1, s12=2, s13=5, s14=15, s15=8, s16=2, s17=3, s18=2, s19=15. This manual is licensed under the GNU General Public License version 3. More information about 6. CONTENTS. 9.11 Adding your own Bayesian network learners...... 157 and one attribute (0 for none) for all test instances. -i. An integrated and easy-touse tool for support vector classification and Since version 2.8, it implements an SMO-type algorithm proposed in this Journal of Machine Learning Research 6, 1889-1918, 2005. options: -s svm_type : set type of SVM (default 0) 0 -C-SVC 1 -nu-SVC 2 -one-class SVM 3 -epsilonSVR 4.
WEKA is a popular machine learning workbench with a development life of nearly two decades. This ... more WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.
This manual is licensed under the GNU General Public License version 2. More information about th... more This manual is licensed under the GNU General Public License version 2. More information about this license can be found at
This manual is licensed under the GNU General Public License version 2. More information about th... more This manual is licensed under the GNU General Public License version 2. More information about this license can be found at
Multi-label classification has rapidly attracted interest in the machine learning literature, and... more Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.
ADAMS is a modular open-source Java framework for developing workflows available for academic res... more ADAMS is a modular open-source Java framework for developing workflows available for academic research as well as commercial applications. It integrates data mining applications, like MOA, WEKA, MEKA and R, image and video processing and feature generation capabilities, spreadsheet and database access, visualizations, GIS, webservices and fast protoyping of new functionality using scripting languages (Groovy/Jython).
Data mining is said to be a field that encourages data to speak for itself rather than “forcing” ... more Data mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for aut...
An Introduction to the WEKA Data Mining System Zdravko Markov Central an extended period of time ... more An Introduction to the WEKA Data Mining System Zdravko Markov Central an extended period of time (the first version of Weka was released 11 years In sum, the Weka team has made an outstanding contribution to the data mining field. 6. s10=0, s11=1, s12=2, s13=5, s14=15, s15=8, s16=2, s17=3, s18=2, s19=15. This manual is licensed under the GNU General Public License version 3. More information about 6. CONTENTS. 9.11 Adding your own Bayesian network learners...... 157 and one attribute (0 for none) for all test instances. -i. An integrated and easy-touse tool for support vector classification and Since version 2.8, it implements an SMO-type algorithm proposed in this Journal of Machine Learning Research 6, 1889-1918, 2005. options: -s svm_type : set type of SVM (default 0) 0 -C-SVC 1 -nu-SVC 2 -one-class SVM 3 -epsilonSVR 4.
WEKA is a popular machine learning workbench with a development life of nearly two decades. This ... more WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.
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