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Learning unions of high-dimensional boxes over the reals

2000
*
Information Processing Letters
*

Thus, we can

doi:10.1016/s0020-0190(00)00024-7
fatcat:zjix2np7o5ed3jzt44winumm2q
*learn*such classes of boxes over infinite domains. ... The running time of the algorithm is polynomial in the logarithm of the size of the domain and other parameters of the target*function*(in particular, the dimension). ... Many natural*functions*can be*represented**as*such boxes and more generally*as*unions of boxes. ...##
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Learning Multiplicity Tree Automata
[chapter]

2006
*
Lecture Notes in Computer Science
*

This is the first time,

doi:10.1007/11872436_22
fatcat:gabzolwfqrenzezsgjfaf6r4ia
*as*far*as*we now, that a*learning*method focuses on non deterministic tree*automata*which computes*functions*over a field. ... In this paper, we present a theoretical approach for the problem of*learning**multiplicity*tree*automata*. These*automata*allows one to define*functions*which compute a number for each tree. ... This is,*as*far*as*we know, the first time that a*learning*method is proposed for*multiplicity*tree*automata*. ...##
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Classification Based on Deep Neural Cellular Automata Model

2019
*
Zenodo
*

The paper discusses how to use deep

doi:10.5281/zenodo.3346722
fatcat:qwtwoulexbe53pwio4pjxxw4ya
*learning*structure for*representing*neural cellular*automata*model. ... A deep*automata*neural cellular system modifies each neuron based on the behavior of the individual and its decision*as*a result of multi-level deep structure*learning*. ... The configuration of deep neural cellular*automata*is a*function*from O[i, j] to S that*represents*2-dimension lattice over set of states S. ...##
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An Automatic Multiple Sclerosis Lesion Segmentation Approach based on Cellular Learning Automata

2019
*
International Journal of Advanced Computer Science and Applications
*

Cellular

doi:10.14569/ijacsa.2019.0100726
fatcat:lw3abm4xbbbhlfxsr7mmqu3ozu
*Learning**Automata*(CLA) is applied on the MRI images with a novel trial and error approach to set penalty and reward frames for each pixel. ... The proposed approach can be considered*as*a supplementary or superior method for other methods such*as*Graph Cuts (GC), fuzzy c-means, mean-shift, k-Nearest Neighbor (KNN), Support Vector Machines (SVM ... Rules in the*learning**automata*can be defined*as*a bit string in which each bit*represents*the next state corresponding to the number of the bit [2] , [5] . IV. ...##
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Fast algorithm for Multiple-Circle detection on images using Learning Automata
[article]

2014
*
arXiv
*
pre-print

On the other hand,

arXiv:1405.5531v1
fatcat:wd53twlmnjgyrjwn75pdycoafi
*Learning**Automata*(LA) is a heuristic method to solve complex multi-modal optimization problems. ... The detection process is considered*as*a multi-modal optimization problem, allowing the detection of*multiple*circular shapes through only one optimization procedure. ... Fast algorithm for*multiple*-circle detection on images using*learning**automata*, IET Image Processing 6 (8) , (2012), pp. 1124-1135 At this work, a circular shape is*represented*by a well-known second degree ...##
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An Intuitionistic Fuzzy Approach to Classify the User Based on an Assessment of the Learner's Knowledge Level in E-Learning Decision-Making

2014
*
Journal of Information Processing Systems
*

Their knowledge on these domain concepts has been collected from tests that were conducted during their

doi:10.3745/jips.04.0011
fatcat:v6dia3uh4jgubn3npntpjzp7cu
*learning*phase. ... In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-*learning*system. ... The membership and nonmembership*functions*are*represented**as*: (u(c), μu(c), υu(c)), (k(c), μk(c), υk(c)), (l(c), μl(c), υl(c)) Where, u(c), k(c), l(c)*represent*unknown, known and*learned*intuitionistic ...##
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Application of S-model learning automata for multi-objective optimal operation of power systems

2005
*
IEE Proceedings - Generation Transmission and Distribution
*

In particular, it is shown that the S-model

doi:10.1049/ip-gtd:20040698
fatcat:wbajbo4yffcyxlqspoxpkktsmu
*learning**automata*can be applied satisfactorily to the multi-objective optimisation problem to obtain the best trade-off between the conflicting objectives of ... Both the generation cost for economic operation and the modal performance measure for stable operation of the power system are considered*as*performance indices for multi-objective optimal operation. ... The procedure in the*learning**automata*is summarised*as*follows: 1. ...##
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Cellular Learning Automata With Multiple Learning Automata in Each Cell and Its Applications

2010
*
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
*

In some applications such

doi:10.1109/tsmcb.2009.2030786
pmid:19884061
fatcat:gktac2ivurhcfpehdjmlht3zri
*as*cellular networks we need to have a model of cellular*learning**automata*for which*multiple**learning**automata*resides in each cell. ... Two applications of this new model such*as*channel assignment in cellular mobile networks and*function*optimization are also given. ... Let α i be the set of actions that is chosen by all*learning**automata*in cell i. Hence, the local rule is*represented*by*function*F i α i+x1 , α i+x2 . . . , α i+xm → β. ...##
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Continuous CLA-EC

2010
*
2010 Fourth International Conference on Genetic and Evolutionary Computing
*

Standard CLA-EC which is introduced recently is an evolutionary computing model obtained by combining cellular

doi:10.1109/icgec.2010.53
fatcat:o3gwyjm6mzgt3oji6mzn76hy64
*learning**automata*(CLA) model and evolutionary computing (EC) model. ... To show the effectiveness of the proposed model it is tested on some*function*optimization problems. ... For CALA, the action probability distribution is*represented*by a continuous*function*and this*function*is updated by*learning*algorithm at any stage. ...##
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Complexity of Equivalence and Learning for Multiplicity Tree Automata
[chapter]

2014
*
Lecture Notes in Computer Science
*

We give a new

doi:10.1007/978-3-662-44522-8_35
fatcat:q3cfxbzvv5henieigbuhhcqjui
*learning*algorithm for*multiplicity*tree*automata*in which counterexamples to equivalence queries are*represented**as*DAGs. ... We consider the query and computational complexity of*learning**multiplicity*tree*automata*in Angluin's exact*learning*model. ... Thus in the context of exact*learning*it is natural to consider a Teacher that can return succinctly-*represented*counterexamples, i.e., trees*represented**as*DAGs. ...##
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Using Learning Automata and Genetic Algorithms to Improve the Quality of Services in Multicast Routing Problem

2012
*
International Journal of Computer Science Engineering and Applications
*

A hybrid

doi:10.5121/ijcsea.2012.2507
fatcat:5dnn4yuymbfltafhtt2emnf3qa
*learning**automata*-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. ... The algorithm complements the advantages of the*learning*Automato Algorithm(LA) and Genetic Algorithm(GA). ... Variable structure*learning**automata*are*represented*by a triple <α,β,T>, where β is the set of inputs, α is the set of actions, and T is*learning*algorithm. ...##
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Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries

2020
*
Annual Conference for Computer Science Logic
*

arises because the running time of the

doi:10.4230/lipics.csl.2020.8
dblp:conf/csl/AngluinAF20
fatcat:gqrbomby4jcytg57umnctwqdzi
*learning*algorithm is bounded*as*a*function*of the size of the representation of the target language, and NBAs (non-deterministic Büchi*automata*) may be exponentially ... interfaces [28], regular model checking [24], finding security bugs [13], code refactoring [27, 31],*learning*verification fixed-points [33],*as*well*as*analyzing botnet protocols [15] and smart card ...*Multiplicity**Automata*A*multiplicity*automaton*represents*a*function*f mapping finite strings Σ * to elements of a field K. ...##
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Complexity of Equivalence and Learning for Multiplicity Tree Automata
[article]

2014
*
arXiv
*
pre-print

,

arXiv:1405.0514v2
fatcat:ey6tl7bo2rc65bh37ywtw2vzya
*represented**as*a tree, that is returned by the Teacher. ... We consider the complexity of equivalence and*learning*for*multiplicity*tree*automata*, i.e., weighted tree*automata*over a field. ... A broad class of such*functions*can be defined by*multiplicity*tree*automata*, which generalise probabilistic tree*automata*. ...##
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Modeling and Simulation using Artificial Neural Network-Embedded Cellular Automata

2020
*
IEEE Access
*

Specifically, a hypothetical model can be constructed through a cellular

doi:10.1109/access.2020.2970547
fatcat:ipwgsqngefhjhnitkmvl5fvdum
*automata*model (simulation modeling), and parameters and*functions*necessary for a hypothetical model can be simulated by*learning*... INDEX TERMS Artificial neural network, big data, cellular*automata*, machine*learning*, modeling and simulation, traffic simulation. ... At this time, parameters and*functions*obtained through machine*learning*can be simulated by applying them to a hypothetical model*represented**as*cellular*automata*. ...##
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Adaptive Finite State Automata and Genetic Algorithms: Merging Individual Adaptation and Population Evolution
[chapter]

2005
*
Adaptive and Natural Computing Algorithms
*

Adaptive finite

doi:10.1007/3-211-27389-1_80
fatcat:jz7jxnf6hjdebcrfhotemquz4y
*automata*, which are basically finite state*automata*that can change their internal structures during operation, have proven to be an attractive way to*represent*simple*learning*strategies ... This paper presents adaptive finite state*automata**as*an alternative formalism to model individuals in a genetic algorithm environment. ... In a GA environment where genotype are*represented**as*an A -FSA , the Baldwin effect could be explored by the appropriate utilization of adaptive*functions*to model plasticity or*learning*. ...
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