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Machine learning (ML) is a field of ... and sometimes more than one is used by the same machine learning system. For example, ... A simple Bayesian network. Rain ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
For example, machine learning has been used for classifying Android malware, [197] for identifying domains belonging to threat actors and for detecting URLs posing a security risk. [198] Research is underway on ANN systems designed for penetration testing, for detecting botnets, [199] credit cards frauds [200] and network intrusions.
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Decision tree learning is a method commonly used in data mining. [3] The goal is to create a model that predicts the value of a target variable based on several input variables. A decision tree is a simple representation for classifying examples.
Perceptrons can be trained by a simple learning algorithm that is usually called the delta rule. It calculates the errors between calculated output and sample output data, and uses this to create an adjustment to the weights, thus implementing a form of gradient descent .
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...