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Machine learning techniques arise largely from statistics and also information theory. In general, entropy is a measure of uncertainty and the objective of machine learning is to minimize uncertainty. Decision tree learning algorithms use relative entropy to determine the decision rules that govern the data at each node. [34]
Name Description Knots [Note 1]Links References trRosettaRNA: trRosettaRNA is an algorithm for automated prediction of RNA 3D structure. It builds the RNA structure by Rosetta energy minimization, with deep learning restraints from a transformer network (RNAformer). trRosettaRNA has been validated in blind tests, including CASP15 and RNA-Puzzles, which suggests that the automated predictions ...
In 2011, authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date". [2] It became quite popular after ranking #1 in the Top 10 Algorithms in Data Mining pre-eminent paper published by Springer LNCS in 2008. [3]
In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains.
PHP Unix, Linux, Windows, iOS 2000 9.7 2022-04-13 Proprietary: T4: Microsoft: Windows 2005 2010 MIT License: Umple: University of Ottawa: cross-platform (Java) 2010 1.35.0 2024-10-11 MIT License: Velocity apache: Apache Software Foundation: cross-platform (Java) 1.6.2 2009-03-19 Apache License 2.0 WaveMaker: WaveMaker Inc. cross platform (aPaaS ...
A USB-pluggable hardware true random number generator. In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), [1] or physical random number generator [2] [3] is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a ...
A new approach to the problem of entropy evaluation is to compare the expected entropy of a sample of random sequence with the calculated entropy of the sample. The method gives very accurate results, but it is limited to calculations of random sequences modeled as Markov chains of the first order with small values of bias and correlations ...
More precisely, the source coding theorem states that for any source distribution, the expected code length satisfies [(())] [ (())], where is the number of symbols in a code word, is the coding function, is the number of symbols used to make output codes and is the probability of the source symbol. An entropy coding attempts to ...