Search Results for "weka mac"
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Showing 66 open source projects for "weka mac"
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Weka
Machine learning software to solve data mining problems
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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MEKA
A Multi-label Extension to Weka
Multi-label classifiers and evaluation procedures using the Weka machine learning framework. -
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ADAMS
ADAMS is a workflow engine for building complex knowledge workflows.
ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA... -
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Varied Tree Weka Package
Random Tree-based classifier featuring multiple node tests.
Weka package extension with custom decision tree classifier (based on Random Tree) generating three different tests in its nodes: traditional value test (e.g. x1 < 3.4), relational test (e.g. x1 < x2) and weighted relational test (e.g. x1 < 0.78 * x2). Decision on node level is made based on weighted vote made by each test. -
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Ceka
Crowd Environment and its Knowledge Analysis
A knowledge analysis tool for crowdsourcing based on Weka. We also have a Python version of Crowdsourcing Learning: CrowdwiseKit on GitHub (https://github.com/tssai-lab/CrowdwiseKit). -
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A.I. Stock Trends With WEKA & TA-Lib
A Repository Of The Java Programs Presented in the Videos.
This is the open/public source code repository for the Java programs shown in the YouTube videos - A.I. Stock Trends With WEKA, TA-Lib and more https://www.youtube.com/channel/UCPxmgFZDS7F06UBBxH5b4mg -
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weka-MTreeClusterer
Flat clustering algorithm based on MTrees implemented for weka.
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Picsart Enterprise Background Removal API for Stunning eCommerce VisualsWith our Remove Background API tool, you can access the transformative capabilities of automation , which will allow you to turn any photo asset into compelling product imagery. With elevated visuals quality on your digital platforms, you can captivate your audience, and therefore achieve higher engagement and sales.
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This site contains packages of sGrid. References: Wells, J.R. and Ting KM, A new simple and efficient density estimator that enables fast systematic search. 2019, PRL, https://doi.org/10.1016/j.patrec.2018.12.020
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weka-RankerByDTClassification
Ranker algorithm based on Decision Trees
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It gives facility of collecting tweets through Twitter Streaming API w.r.t different search criteria and to save tweets in CSV and ARFF (WEKA) file formats.
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Open Cezeri Library
Effective Linear Algebra and Computer Vision Library with JAVA
...- Vision: It can access web cams, imaging source industrial cameras for manuel settings and advanced issues. Studies on accesing Leapmotion and Kinect is still under-development. 2- Machine learning: It uses Weka Software tool and some personel coded ML algorithms 3- CMatrix: Special matrix library called as CMatrix meaning Cezeri Maztrix Class. Actually it is regarded as the core of the OCL. CMatrix supports fluent interface and method chaining. -
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This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian...
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Mulan is a java library for learning from multi-label data. It is built on top of the Weka library and currently offers a variety of classification, ranking and feature selection algorithms, an evaluation framework that calculates several measures.
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JCLALtext
Text processing module for JCLAL
JCLALtext is a class library designed to extend the framework JCLAL text tasks. JCLALtext is free, open source and developed with the Java programming language. JCLALtext is distributed under the GNU license. The researcher can use the class library by adding it to your project. -
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WekaBioSimilarity is an extension of Weka that implements several measures to compare binary feature vectors; and, additionally, extrapolates those measures to work with multi-value, string and numerical feature vectors.
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HybridPSO
HybridPSO algorithm integrated with weka
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gBestPSO
gBestPSO algorithm integrated with weka
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AntMiner
AntMiner algorithm integrated with weka
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MODLEM
rule-based, WEKA compatible, Machine Learning algorithm
This project is a WEKA (Waikato Environment for Knowledge Analysis) compatible implementation of MODLEM - a Machine Learning algorithm which induces minimum set of rules. These rules can be adopted as a classifier (in terms of ML). It is a sequential covering algorithm, which was invented to cope with numeric data without discretization. Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule... -
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WekaBinaryMeasures is an extension of Weka that implements several measures to compare binary feature vectors; and, additionally, extrapolates those measures to work with multi-value, string and numerical feature vectors. Visit the wiki for information about installation and examples: https://sourceforge.net/p/wekabinarymeasures/wiki/Home/
