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  2. Probably approximately correct learning - Wikipedia

    en.wikipedia.org/wiki/Probably_approximately...

    In computational learning theory, probably approximately correct ( PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.

  3. Partially observable Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Partially_observable...

    A partially observable Markov decision process ( POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a sensor model (the probability ...

  4. Computable function - Wikipedia

    en.wikipedia.org/wiki/Computable_function

    Computable functions are the formalized analogue of the intuitive notion of algorithms, in the sense that a function is computable if there exists an algorithm that can do the job of the function, i.e. given an input of the function domain it can return the corresponding output. Computable functions are used to discuss computability without ...

  5. Saddle point - Wikipedia

    en.wikipedia.org/wiki/Saddle_point

    Saddle point. In mathematics, a saddle point or minimax point [1] is a point on the surface of the graph of a function where the slopes (derivatives) in orthogonal directions are all zero (a critical point ), but which is not a local extremum of the function. [2] An example of a saddle point is when there is a critical point with a relative ...

  6. Kernel principal component analysis - Wikipedia

    en.wikipedia.org/wiki/Kernel_principal_component...

    Kernel principal component analysis. In the field of multivariate statistics, kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space .

  7. Vapnik–Chervonenkis dimension - Wikipedia

    en.wikipedia.org/wiki/Vapnik–Chervonenkis...

    Vapnik–Chervonenkis dimension. In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the size (capacity, complexity, expressive power, richness, or flexibility) of a class of sets. The notion can be extended to classes of binary functions. It is defined as the cardinality of the largest set of points that ...

  8. Description logic - Wikipedia

    en.wikipedia.org/wiki/Description_logic

    A description logic (DL) models concepts, roles and individuals, and their relationships. The fundamental modeling concept of a DL is the axiom —a logical statement relating roles and/or concepts. [2] This is a key difference from the frames paradigm where a frame specification declares and completely defines a class.

  9. C (programming language) - Wikipedia

    en.wikipedia.org/wiki/C_(programming_language)

    C ( pronounced / ˈsiː / – like the letter c) [ 6 ] is a general-purpose programming language. It was created in the 1970s by Dennis Ritchie and remains very widely used and influential. By design, C's features cleanly reflect the capabilities of the targeted CPUs. It has found lasting use in operating systems code (especially in kernels [ 7 ...