Displaying similar documents to “PAC learning under helpful distributions”

PAC Learning under Helpful Distributions

François Denis, Rémi Gilleron (2010)

RAIRO - Theoretical Informatics and Applications

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A PAC teaching model -under helpful distributions -is proposed which introduces the classical ideas of teaching models within the PAC setting: a polynomial-sized teaching set is associated with each target concept; the criterion of success is PAC identification; an additional parameter, namely the inverse of the minimum probability assigned to any example in the teaching set, is associated with each distribution; the learning algorithm running time takes this new parameter into...

Combined classifier based on feature space partitioning

Michał Woźniak, Bartosz Krawczyk (2012)

International Journal of Applied Mathematics and Computer Science

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This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier,...

Epoch-incremental reinforcement learning algorithms

Roman Zajdel (2013)

International Journal of Applied Mathematics and Computer Science

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In this article, a new class of the epoch-incremental reinforcement learning algorithm is proposed. In the incremental mode, the fundamental TD(0) or TD(λ) algorithm is performed and an environment model is created. In the epoch mode, on the basis of the environment model, the distances of past-active states to the terminal state are computed. These distances and the reinforcement terminal state signal are used to improve the agent policy.

Multiple-instance learning with pairwise instance similarity

Liming Yuan, Jiafeng Liu, Xianglong Tang (2014)

International Journal of Applied Mathematics and Computer Science

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Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency...

Towards a theory of practice in metaheuristics design: A machine learning perspective

Mauro Birattari, Mark Zlochin, Marco Dorigo (2006)

RAIRO - Theoretical Informatics and Applications

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A number of methodological papers published during the last years testify that a need for a thorough revision of the research methodology is felt by the operations research community – see, for example, [Barr (1995) 9–32; Eiben and Jelasity, 582–587; Hooker, (1995) 33–42; Rardin and Uzsoy, (2001) 261–304]. In particular, the performance evaluation of nondeterministic methods, including widely studied metaheuristics such as evolutionary...

Application of agent-based simulated annealing and tabu search procedures to solving the data reduction problem

Ireneusz Czarnowski, Piotr Jędrzejowicz (2011)

International Journal of Applied Mathematics and Computer Science

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The problem considered concerns data reduction for machine learning. Data reduction aims at deciding which features and instances from the training set should be retained for further use during the learning process. Data reduction results in increased capabilities and generalization properties of the learning model and a shorter time of the learning process. It can also help in scaling up to large data sources. The paper proposes an agent-based data reduction approach with the learning...

Minimal decision rules based on the apriori algorithm

María Fernández, Ernestina Menasalvas, Óscar Marbán, José Peña, Socorro Millán (2001)

International Journal of Applied Mathematics and Computer Science

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Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing...

Application of the Rasch model in categorical pedigree analysis using MCEM: I binary data

G. Qian, R. M. Huggins, D. Z. Loesch (2004)

Discussiones Mathematicae Probability and Statistics

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An extension of the Rasch model with correlated latent variables is proposed to model correlated binary data within families. The latent variables have the classical correlation structure of Fisher (1918) and the model parameters thus have genetic interpretations. The proposed model is fitted to data using a hybrid of the Metropolis-Hastings algorithm and the MCEM modification of the EM-algorithm and is illustrated using genotype-phenotype data on a psychological subtest in families...