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The central principle of D3.js design is to enable the programmer to first use a CSS-style selector to select a given set of Document Object Model (DOM) nodes, then use operators to manipulate them in a similar manner to jQuery. [8]
Vega acts as a low-level language suited to explanatory figures (the same use case as D3.js), while Vega-Lite is a higher-level language suited to rapidly exploring data. [3] Vega is used in the back end of several data visualization systems, for example Voyager.
MALLET is an integrated collection of Java code useful for statistical natural language processing, document classification, cluster analysis, information extraction, topic modeling and other machine learning applications to text.
Neural Designer is a data mining software based on deep learning techniques written in C++. Orange is a similar open-source project for data mining, machine learning and visualization based on scikit-learn. RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka. scikit-learn is a popular machine learning ...
Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). [ 2 ] [ 3 ] It is a framework with wide support for deep learning algorithms. [ 4 ] Deeplearning4j includes implementations of the restricted Boltzmann machine , deep belief net , deep autoencoder, stacked denoising autoencoder and recursive ...
Whereas the original systems were focussed on medical diagnosis the applicability of the approach was generalized by the successor D3 [4]. [5] As the predecessors were implemented in the LISP programming language, d3web [ 6 ] is a full Java re-implementation.
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of objects in a set into a configuration of points mapped into an abstract Cartesian space.
Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict the output from future input.