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  2. Algorithmic inference - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_inference

    Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory , granular computing , bioinformatics , and, long ago, structural probability ( Fraser 1966 ).

  3. Grammar induction - Wikipedia

    en.wikipedia.org/wiki/Grammar_induction

    Grammar induction (or grammatical inference) [1] is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.

  4. Characteristic samples - Wikipedia

    en.wikipedia.org/wiki/Characteristic_samples

    The inference algorithm gets the sample and computes a representation consistent with the sample. The goal is that when the inference algorithm receives a characteristic sample for a language L {\displaystyle L} , or a sample that subsumes a characteristic sample for L {\displaystyle L} , it will return a representation that recognizes exactly ...

  5. Backward chaining - Wikipedia

    en.wikipedia.org/wiki/Backward_chaining

    Because the list of goals determines which rules are selected and used, this method is called goal-driven, in contrast to data-driven forward-chaining inference. The backward chaining approach is often employed by expert systems. Programming languages such as Prolog, Knowledge Machine and ECLiPSe support backward chaining within their inference ...

  6. Automated machine learning - Wikipedia

    en.wikipedia.org/wiki/Automated_machine_learning

    Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.

  7. Automated reasoning - Wikipedia

    en.wikipedia.org/wiki/Automated_reasoning

    John Pollock's OSCAR system [2] is an example of an automated argumentation system that is more specific than being just an automated theorem prover. Tools and techniques of automated reasoning include the classical logics and calculi, fuzzy logic, Bayesian inference, reasoning with maximal entropy and many less formal ad hoc techniques.

  8. Today's Wordle Hint, Answer for #1270 on Tuesday, December 10 ...

    www.aol.com/todays-wordle-hint-answer-1270...

    If you’re stuck on today’s Wordle answer, we’re here to help—but beware of spoilers for Wordle 1270 ahead. Let's start with a few hints.

  9. Knowledge representation and reasoning - Wikipedia

    en.wikipedia.org/wiki/Knowledge_representation...

    Also, data structures and algorithms for general fast search. In this area, there is a strong overlap with research in data structures and algorithms in computer science. In early systems, the Lisp programming language, which was modeled after the lambda calculus, was often used as a form of functional knowledge representation. Frames and Rules ...