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Learning unions of high-dimensional boxes over the reals

Amos Beimel, Eyal Kushilevitz
2000 Information Processing Letters  
Thus, we can 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.  ... 
doi:10.1016/s0020-0190(00)00024-7 fatcat:zjix2np7o5ed3jzt44winumm2q

Learning Multiplicity Tree Automata [chapter]

Amaury Habrard, Jose Oncina
2006 Lecture Notes in Computer Science  
This is the first time, 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.  ... 
doi:10.1007/11872436_22 fatcat:gabzolwfqrenzezsgjfaf6r4ia

Classification Based on Deep Neural Cellular Automata Model

Yasser F. Hassan
2019 Zenodo  
The paper discusses how to use deep 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.  ... 
doi:10.5281/zenodo.3346722 fatcat:qwtwoulexbe53pwio4pjxxw4ya

An Automatic Multiple Sclerosis Lesion Segmentation Approach based on Cellular Learning Automata

Mohammad Moghadasi, Dr. Gabor
2019 International Journal of Advanced Computer Science and Applications  
Cellular 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.  ... 
doi:10.14569/ijacsa.2019.0100726 fatcat:lw3abm4xbbbhlfxsr7mmqu3ozu

Fast algorithm for Multiple-Circle detection on images using Learning Automata [article]

Erik Cuevas, Fernando Wario, Valentin Osuna, Daniel Zaldivar, Marco Perez
2014 arXiv   pre-print
On the other hand, 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  ... 
arXiv:1405.5531v1 fatcat:wd53twlmnjgyrjwn75pdycoafi

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 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  ... 
doi:10.3745/jips.04.0011 fatcat:v6dia3uh4jgubn3npntpjzp7cu

Application of S-model learning automata for multi-objective optimal operation of power systems

B.H. Lee, K.Y. Lee
2005 IEE Proceedings - Generation Transmission and Distribution  
In particular, it is shown that the S-model 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.  ... 
doi:10.1049/ip-gtd:20040698 fatcat:wbajbo4yffcyxlqspoxpkktsmu

Cellular Learning Automata With Multiple Learning Automata in Each Cell and Its Applications

H. Beigy, M.R. Meybodi
2010 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
In some applications such 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.  ... 
doi:10.1109/tsmcb.2009.2030786 pmid:19884061 fatcat:gktac2ivurhcfpehdjmlht3zri

Continuous CLA-EC

S Zanganeh, M R Meybodi, M H Sedehi
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 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.  ... 
doi:10.1109/icgec.2010.53 fatcat:o3gwyjm6mzgt3oji6mzn76hy64

Complexity of Equivalence and Learning for Multiplicity Tree Automata [chapter]

Ines Marusic, James Worrell
2014 Lecture Notes in Computer Science  
We give a new 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.  ... 
doi:10.1007/978-3-662-44522-8_35 fatcat:q3cfxbzvv5henieigbuhhcqjui

Using Learning Automata and Genetic Algorithms to Improve the Quality of Services in Multicast Routing Problem

Mohammad Reza Karami Nejad
2012 International Journal of Computer Science Engineering and Applications  
A hybrid 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.  ... 
doi:10.5121/ijcsea.2012.2507 fatcat:5dnn4yuymbfltafhtt2emnf3qa

Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries

Dana Angluin, Timos Antonopoulos, Dana Fisman, Michael Wagner
2020 Annual Conference for Computer Science Logic  
arises because the running time of the 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.  ... 
doi:10.4230/lipics.csl.2020.8 dblp:conf/csl/AngluinAF20 fatcat:gqrbomby4jcytg57umnctwqdzi

Complexity of Equivalence and Learning for Multiplicity Tree Automata [article]

Ines Marusic, James Worrell
2014 arXiv   pre-print
, 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.  ... 
arXiv:1405.0514v2 fatcat:ey6tl7bo2rc65bh37ywtw2vzya

Modeling and Simulation using Artificial Neural Network-Embedded Cellular Automata

Byeong Soo Kim, Tag Gon Kim
2020 IEEE Access  
Specifically, a hypothetical model can be constructed through a cellular 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.  ... 
doi:10.1109/access.2020.2970547 fatcat:ipwgsqngefhjhnitkmvl5fvdum

Adaptive Finite State Automata and Genetic Algorithms: Merging Individual Adaptation and Population Evolution [chapter]

H. Pistori, P. S. Martins, A. A. de Castro
2005 Adaptive and Natural Computing Algorithms  
Adaptive finite 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.  ... 
doi:10.1007/3-211-27389-1_80 fatcat:jz7jxnf6hjdebcrfhotemquz4y
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