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ML.NET is a free software machine learning library for the C# and F# programming languages. [ 4 ] [ 5 ] [ 6 ] It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification ...
Standard ML ( SML) is a general-purpose, high-level, modular, functional programming language with compile-time type checking and type inference. It is popular for writing compilers, for programming language research, and for developing theorem provers . Standard ML is a modern dialect of ML, the language used in the Logic for Computable ...
ML (Meta Language) is a general-purpose, high-level, functional programming language.It is known for its use of the polymorphic Hindley–Milner type system, which automatically assigns the data types of most expressions without requiring explicit type annotations (type inference), and ensures type safety; there is a formal proof that a well-typed ML program does not cause runtime type errors. [1]
t. e. Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [ 1] Recently, artificial neural networks have been able to surpass many previous approaches ...
OCaml ( / oʊˈkæməl / oh-KAM-əl, formerly Objective Caml) is a general-purpose, high-level, multi-paradigm programming language which extends the Caml dialect of ML with object-oriented features. OCaml was created in 1996 by Xavier Leroy, Jérôme Vouillon, [5] Damien Doligez, Didier Rémy, [6] Ascánder Suárez, and others.
Machine learningand data mining. These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning ), computer hardware, and, less ...
Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...
e. In machine learningand pattern recognition, a featureis an individual measurable property or characteristic of a phenomenon.[1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classificationand regression.