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  2. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

    In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. [1]

  3. Learning rule - Wikipedia

    en.wikipedia.org/wiki/Learning_rule

    Sometimes only when the Widrow-Hoff is applied to binary targets specifically, it is referred to as Delta Rule, but the terms seem to be used often interchangeably. The delta rule is considered to a special case of the back-propagation algorithm. Delta rule also closely resembles the Rescorla-Wagner model under which Pavlovian conditioning ...

  4. Computational learning theory - Wikipedia

    en.wikipedia.org/wiki/Computational_learning_theory

    Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. For example, the samples might be descriptions of mushrooms, and the labels could be whether or not the mushrooms are edible.

  5. CN2 algorithm - Wikipedia

    en.wikipedia.org/wiki/CN2_algorithm

    The CN2 induction algorithm is a learning algorithm for rule induction. [1] It is designed to work even when the training data is imperfect. It is based on ideas from the AQ algorithm and the ID3 algorithm. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.

  6. ADALINE - Wikipedia

    en.wikipedia.org/wiki/ADALINE

    Learning inside a single-layer ADALINE Photo of an ADALINE machine, with hand-adjustable weights implemented by rheostats Schematic of a single ADALINE unit [1]. ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented it.

  7. Rule-based machine learning - Wikipedia

    en.wikipedia.org/wiki/Rule-based_machine_learning

    Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...

  8. Sample complexity - Wikipedia

    en.wikipedia.org/wiki/Sample_complexity

    A learning algorithm over is a computable map from to . In other words, it is an algorithm that takes as input a finite sequence of training samples and outputs a function from X {\displaystyle X} to Y {\displaystyle Y} .

  9. Python syntax and semantics - Wikipedia

    en.wikipedia.org/wiki/Python_syntax_and_semantics

    A snippet of Python code with keywords highlighted in bold yellow font. The syntax of the Python programming language is the set of rules that defines how a Python program will be written and interpreted (by both the runtime system and by human readers). The Python language has many similarities to Perl, C, and Java. However, there are some ...