JML is a lightweight, easy-to-use and powerful Java library for developing machine learning tools. JML is based on the lightweight and easy to use Matlab library MLJava which was founded by Tim Davis. MLJava is a remarkable feat; a library that allows a user to achieve machine learning-related tasks with the same ease as they would in Matlab. Although many efforts have been made to simplify machine learning in JAVA, MLJava is so far the easiest and most effective one.

Features:
Feature training
Feature selection
Model Evaluation

See also
MLlib

References

External links

Category:Free software programmed in Java (programming language)
Category:Machine learning
Category:Java platform software
Category:Software using the GPL licenseAge-related changes in the myocardial response to autonomic stimulation.
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JML is an easy to use, lightweight and elegant machine learning toolkit. You can easily use it to implement many machine learning related algorithms in Java.

JML is a java based machine learning library aimed to build tools and algorithms to accomplish machine learning tasks.
JML provides a set of functions for doing clustering, linear regression, classification, and probabilistic inference; it also provides a simulation framework that allows the development of new algorithms, and can be used to test their performance.

JML can be used to easily build a website or a cloud machine learning solution.
Supporting a large selection of classical and modern model learners, implemented in C, C++ and Java, JML combines a simple interface and a high performance.
The toolkit provides various classes to allow you to easily implement machine learning models into your java programs.
JML provides you with a simple and clean API that works in combination with a Javadoc API to allow the development of advanced applications.

The library is open source and available under the open source BSD license. There is a separate development version that is also available under a commercial license.
JML is available on Google code:

The github project is available at:

If you find it useful you can also help improving it.

JML is written in Java and is distributed under a BSD license.

JML is not redistributable without a commercial license. To be granted a commercial license contact the project developer, Santiago Calatrava or contact me at at cs30080515@utu.fi

JML uses Apache License v2. When you use this library in applications
You must attribute the use of JML in your applications and also include the license header of the respective license as follows:
/**
* This function was created by Santiago Calatrava
*
*
*
* @
2f7fe94e24

JML is an open-source library available to the general public. It is a Java implementation of the JML algorithm, a Matlab library for Machine Learning (
JML is especially suited for:
– Machine Learning, Statistical Analysis
– Neural Network Design and Optimization
– Data Structure Design
JML is intended to be a better and faster alternative to Matlab.
What is JML?
JML is a Java implementation of the JML algorithm, a Matlab library for Machine Learning. In particular, it contains an implementation of many prediction and optimization algorithms, data structure design and utility functions.
FEATURES
Integration with the Java platform
Easy to use
Easy to deploy
The JML algorithm supports the full range of optimization and classification algorithms for the Artificial Neural Network (ANN) approach. JML provides solution to the most important optimization problems:
– Training/Test/Parameters tuning
– Optimization, qeustion
– Posterior graph estimation
– Learning
Optimization and Classification
JML provides implementations for a wide range of optimization and classification algorithms.
– Simple yet powerful (link)
– Fast and flexible (link)
JML was designed to be as simple and powerful as possible, and to be as fast and flexible as possible. This is reflected in the fact that there are only two state abstractions: CLOSURE for the closure and STATE for the state.
Under the hood, the JML implementation is based on java.util.concurrent.locks.AllocationLock.
JML provides you with an easy to use interface to many different libraries/softwares (more than 18 algorithms, more than 25 datasets, more than 20 statistical functions).
Advantages/Benefits
JML allows you to quickly translate matlab code to Java code. You can run your code on the PC or on the Web.
Advantages/Benefits (example)
– Your mission
– Cool your laptop with free energy
A great example of how JML can be used to solve problems is the news. In journalism there are many data related tasks such as, data preprocessing, data mining or time series prediction. If you need data mining algorithms, plotting, data management, or time series methods, JML is a perfect solution.
Advantages/Benefits (example)
– Time series forecast
– Time series prediction
–

JML is a lightweight and easy to use Java library especially designed for implementing machine learning tools.
JML enables you to quickly translate code from Matlab to Java. It features optimization algorithms, logistic regression, spectral clustering, generative modeling and classification.
It can be downloaded at
JML is easy to use, has a small core and a wide selection of tools for machine learning research.
The implementation is very fast as it uses Java native libraries.
JML includes a JAR file to be used as a library in any Java project.
Usage Example:
Java Code:
//Create an empty ML project
MLProject mlb = new MLProject();

//Create a multinomial logistic regression algorithm
MLRegression algorithm = new MLRegression();

//Fit the model using the examples provided in the data source
algorithm.setTrainingDataSet(mbl.getClassesDataSet(“/DataSet/DataSource/users/models/multinomialLogisticRegression”));
//Set the return classes to response variables
algorithm.setClassification(mbl.getClassesDataSet(“/DataSet/DataSource/users/models/multinomialLogisticRegression”).getResponseVariables());
//Set the algorithm parameters
algorithm.setNumPartitions(9);
algorithm.setOptimizerMethod(“L-BFGS-B”);
//Set the optimization options
algorithm.setOptimizationOptions(new OptimizationOptions(AlgorithmOptions.METHOD_NONE, AlgorithmOptions.OPTIMAL_TOLERANCE_FALSE));
//Set the convergence criteria for the model
algorithm.setConvergenceThreshold(0.001);
//Fit the model and score it
algorithm.score(mbl.getClassesDataSet(“/DataSet/DataSource/users/models/multinomialLogisticRegression”));

//Create a logistic regression classifier using the model

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