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  2. Turn-by-turn navigation - Wikipedia

    en.wikipedia.org/wiki/Turn-by-turn_navigation

    Turn-by-turn navigation is a feature of some satellite navigation devices where directions for a selected route are continually presented to the user in the form of spoken or visual instructions. The system keeps the user up-to-date about the best route to the destination, and is often updated according to changing factors such as traffic and ...

  3. Graphical model - Wikipedia

    en.wikipedia.org/wiki/Graphical_model

    e. A graphical model or probabilistic graphical model ( PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics —particularly Bayesian statistics —and machine learning .

  4. Inductive bias - Wikipedia

    en.wikipedia.org/wiki/Inductive_bias

    Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g. step-functions in decision trees instead of continuous function in a linear regression model ). Learning is the process of apprehending useful knowledge by observing and interacting with the world. [2]

  5. 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 ...

  6. Bayesian network - Wikipedia

    en.wikipedia.org/wiki/Bayesian_network

    Automatically learning the graph structure of a Bayesian network (BN) is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl and rests on the distinction between the three possible patterns allowed in a 3-node DAG:

  7. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    t. e. Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called ...

  8. Computational learning theory - Wikipedia

    en.wikipedia.org/wiki/Computational_learning_theory

    Online machine learning, from the work of Nick Littlestone [citation needed]. While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms. For example, PAC theory inspired boosting, VC theory led to support vector machines, and Bayesian inference led to belief ...

  9. Heuristic (computer science) - Wikipedia

    en.wikipedia.org/wiki/Heuristic_(computer_science)

    Heuristic (computer science) In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover") is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space.