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Neural Computation, 1992
Very rarely are training data evenly distributed in the input space. Local learning algorithms attempt to locally adjust the capacity of the training system to the properties of the training set in each area of the input space. The family of local learning algorithms contains known methods, like the k-nearest neighbors method (kNN) or the radial basis
Léon Bottou, Vladimir Vapnik
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Very rarely are training data evenly distributed in the input space. Local learning algorithms attempt to locally adjust the capacity of the training system to the properties of the training set in each area of the input space. The family of local learning algorithms contains known methods, like the k-nearest neighbors method (kNN) or the radial basis
Léon Bottou, Vladimir Vapnik
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On universal learning algorithms
Information Processing Letters, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Oded Goldreich 0001, Dana Ron
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Orthonormalization Learning Algorithms
2007 International Joint Conference on Neural Networks, 2007Orthonormalization is an essential stabilizing task in many signal processing algorithms and can be accomplished using the Gram-Schmidt process. In this paper, dynamical systems for orthonormalization are proposed. These systems converge to the desired limits without computing matrix square root.
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Computer Music Journal, 2004
In this article I describe a computer program called Gradus (after Johann Joseph Fux’s 1725 treatise Gradus ad Parnassum) that initially analyzes a set of model two-voice, one-against-one, first-species counterpoints in order to produce a series of compositional goals.
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In this article I describe a computer program called Gradus (after Johann Joseph Fux’s 1725 treatise Gradus ad Parnassum) that initially analyzes a set of model two-voice, one-against-one, first-species counterpoints in order to produce a series of compositional goals.
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Online Pairwise Learning Algorithms
Neural Computation, 2016Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing
Yiming Ying, Ding-Xuan Zhou
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Teaching Algorithms and Learning Algorithms
Programmed Learning and Educational Technology, 1982Abstract *A11 teaching processes can be precisely specified by means of Helmar Frank's six didactic variables (each of which can in turn be interpreted as a vector of vectors). These are: learning system, teaching system, subject matter, target standard, environment and teaching algorithm.
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Learning Convergence of CMAC Algorithm
Neural Processing Letters, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chao He, Lixin Xu, Yuhe Zhang
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Deep Learning for Algorithm Portfolios
Proceedings of the AAAI Conference on Artificial Intelligence, 2016It is well established that in many scenarios there is no single solver that will provide optimal performance across a wide range of problem instances. Taking advantage of this observation, research into algorithm selection is designed to help identify the best approach for each problem at hand.
Loreggia Andrea +3 more
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Learning in genetic algorithms
1998Learning in artificial neural networks is often cast as the problem of “teaching” a set of stimulus-response (or input-output) pairs to an appropriate mathematical model which abstracts certain known properties of neural networks. A paradigm which has been developed independently of neural network models are genetic algorithms (GA).
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A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
IEEE Access, 2022Ibomoiye Domor Mienye, Yanxia Sun
exaly