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Committee Machine
A committee machine is a type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response.HAYKIN, S. Neural Networks - A Comprehensive Foundation. Second edition. Pearson Prentice Hall: 1999. The combined response of the committee machine is supposed to be superior to those of its constituent experts. Compare with ensembles of classifiers. Types Static structures In this class of committee machines, the responses of several predictors (experts) are combined by means of a mechanism that does not involve the input signal, hence the designation static. This category includes the following methods: *Ensemble averaging In ensemble averaging, outputs of different predictors are linearly combined to produce an overall output. * Boosting In boosting, a weak algorithm is converted into one that achieves arbitrarily high accuracy. Dynamic structures In this second class of committee ma ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Divide And Rule
Divide and rule policy ( la, divide et impera), or divide and conquer, in politics and sociology is gaining and maintaining power divisively. Historically, this strategy was used in many different ways by empires seeking to expand their territories. However, it has been hard to distinguish between the exploitation of pre-existing divisions by opponents, and the deliberate creation or strengthening of these divisions implied by "divide and rule". The strategy, but not the phrase, applies in many ancient cases: the example of Aulus Gabinius exists, parting the Jewish nation into five conventions, reported by Flavius Josephus in Book I, 169–170 of ''The Jewish War'' (''De bello Judaico''). Strabo also reports in ''Geographica'', 8.7.3 that the Achaean League was gradually dissolved when it became part of the Roman province of Macedonia, as the Romans treated the various states differently, wishing to preserve some and to destroy others. Elements of this technique involve: * ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ensembles Of Classifiers
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives. Overview Supervised learning algorithms perform the task of searching through a hypothesis space to find a suitable hypothesis that will make good predictions with a particular problem. Even if the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensembles combine multiple hypotheses to form a (hopefully) better hypothesis. The term ''ensemble'' is usually reserved for methods that generate multiple hypotheses using the same base learne ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ensemble Averaging
In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out." Overview Ensemble averaging is one of the simplest types of committee machines. Along with boosting, it is one of the two major types of static committee machines.Haykin, Simon. Neural networks : a comprehensive foundation. 2nd ed. Upper Saddle River N.J.: Prentice Hall, 1999. In contrast to standard network design in which many networks are generated but only one is kept, ensemble averaging keeps the less satisfactory networks around, but with less weight.Hashem, S. "Optimal linear combinations of neural networks." Neural Networks 10, no. 4 (1997): 599–614. The theory of ensemble averaging relies on two properties o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Boosting (meta-algorithm)
In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989):Michael Kearns(1988)''Thoughts on Hypothesis Boosting'' Unpublished manuscript (Machine Learning class project, December 1988) "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. Robert Schapire's affirmative answer in a 1990 paper to the question of Kearns and Valiant has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting. When first introduced, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mixture Of Experts
Mixture of experts (MoE) refers to a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous regions. It differs from ensemble techniques in that typically only a few, or 1, expert model will be run, rather than combining results from all models. An example from computer vision is combining one neural network model for human detection with another for pose estimation. Hierarchical mixture If the output is conditioned on multiple levels of (probabilistic) gating functions, the mixture is called a hierarchical mixture of experts. A gating network decides which expert to use for each input region. Learning thus consists of learning the parameters of: * individual learners and * gating network. Applications Meta uses MoE in its NLLB-200 system. It uses multiple MoE models that share capacity for use by low-resource language models A language model is a probability distribution over sequences of words. Given ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Networks
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
