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A Study of Structural and Parametric Learning in XCS

Tim Kovacs, Manfred Kerber
2006 Evolutionary Computation  
The two components interleave and in the case of XCS drive the population toward a minimal, fit, non-overlapping population.  ...  We compare XCS to a system in which  ...  First, it evolves new structures by generating new rules in a genetic process (structural learning); second, it adjusts parameters of existing rules (parametric learning), for example, rule prediction  ... 
doi:10.1162/evco.2006.14.1.1 pmid:16536886 fatcat:mcd5digomfegdani2t6eot35ku

Distributed classifier migration in xcs for classification of electroencephalographic signals

B. T. Skinner, H. T. Nguyen, D. K. Liu
2007 2007 IEEE Congress on Evolutionary Computation  
Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification  ...  This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier  ...  ACKNOWLEDGMENTS The authors wish to thank Professor Ashley Craig and Dr Yvonne Tran for support in conducting the EEG trials and Dr Matthew Gaston for establishing, maintaining and supporting the UTS Engineering  ... 
doi:10.1109/cec.2007.4424830 dblp:conf/cec/SkinnerNL07 fatcat:aweojp5k6bcarhpg3spg3wbfki

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

S. García, A. Fernández, J. Luengo, F. Herrera
2008 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
The experimental analysis on the performance of a proposed method is a crucial and necessary task to carry out in a research.  ...  It presents a study involving a set of techniques which can be used for doing a rigorous comparison among algorithms, in terms of obtaining successful classification models.  ...  Following the same structure as in the previous section, the basic and advanced non-parametrical tests for multiple comparisons are described in Sect. 6.1 and their application on the case study is conducted  ... 
doi:10.1007/s00500-008-0392-y fatcat:t6a23jd2cfawhb7xsq6esvidbe

Neural network interpolation of exchange-correlation functional [article]

Alexander Ryabov, Iskander Akhatov, Petr Zhilyaev
2019 arXiv   pre-print
In contrast, the neural network (NN) approach can provide a general way to parametrize an XC functional without any a priori knowledge of its functional form.  ...  Many different developed parametrizations mainly utilize a number of phenomenological rules to construct a specific XC functional.  ...  To test the NN XCs for real physical systems, we consider Si in a diamond structure with 8 basis atoms in a cubic cell (lattice constant: 10.2 a 0 ).  ... 
arXiv:1909.03860v2 fatcat:c3jtmhtoeffsrlkl6ncezjl7k4

Special issue on advances in Learning Classifier Systems

Kamran Shafi, Ryan Urbanowicz, Muhammad Iqbal
2013 Evolutionary Intelligence  
In Function Approximation with LWPR and XCSF: A Comparative Study, Stalph et al. focus on XCSF, a variant of XCS for function approximation that has recently received a large amount of attention.  ...  Taking a look back to the origins of LCSs, in Risk Neutrality in Learning Classifier Systems Smith develops a novel model of risk-neutral reinforcement learning in a traditional Bucket Brigade credit-allocation  ... 
doi:10.1007/s12065-013-0097-8 fatcat:3dzvs7clwvbujb4gdvwvg3j64i

Special issue on advances in learning classifier systems

Daniele Loiacono, Albert Orriols-Puig, Ryan Urbanowicz
2012 Evolutionary Intelligence  
In Function Approximation with LWPR and XCSF: A Comparative Study, Stalph et al. focus on XCSF, a variant of XCS for function approximation that has recently received a large amount of attention.  ...  Taking a look back to the origins of LCSs, in Risk Neutrality in Learning Classifier Systems Smith develops a novel model of risk-neutral reinforcement learning in a traditional Bucket Brigade credit-allocation  ... 
doi:10.1007/s12065-012-0081-8 fatcat:xq6mnmtyeffptg7y4giufpgji4

Neural network interpolation of exchange-correlation functional

Alexander Ryabov, Iskander Akhatov, Petr Zhilyaev
2020 Scientific Reports  
In contrast, the neural network (NN) approach can provide a general way to parametrize an XC functional without any a priori knowledge of its functional form.  ...  Many different developed parametrizations mainly utilize a number of phenomenological rules to construct a specific XC functional.  ...  equation in the framework of DFT for a Si diamond structure with 8 basis atoms in a cubic cell.  ... 
doi:10.1038/s41598-020-64619-8 pmid:32409657 fatcat:lcmjfzgwjfcancqvcenteyq4hi

Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes [article]

Feras Saad, Vikash Mansinghka
2017 arXiv   pre-print
from statistics and machine learning, on a real-world database of over 300 sparsely observed indicators of macroeconomic development and public health.  ...  conditional mutual information between arbitrary subsets of variables, subject to a broad class of constraints.  ...  N000141310333), the Army Research Office (under agreement number W911NF-13-1-0212), and gifts from Analog Devices and Google.  ... 
arXiv:1611.01708v2 fatcat:skzsiytqijeabludcmogli2ew4

Learning-based Natural Geometric Matching with Homography Prior [article]

Yifang Xu, Tianli Liao, Jing Chen
2018 arXiv   pre-print
Furthermore, a novel loss function with Gaussian weights guarantees the model accuracy and efficiency in training procedure.  ...  Experimental results on Proposal Flow dataset show that our method outperforms state-of-the-art methods, both in terms of alignment accuracy and naturalness.  ...  Future works include making the separate training of homography and other transformations in a unified framework. Table 2 2 Ablation studies for single homogrpahy.  ... 
arXiv:1807.05119v1 fatcat:kv4w2giy4jbr5lcqq2yh765owy

SEGAN: Speech Enhancement Generative Adversarial Network [article]

Santiago Pascual, Antonio Bonafonte, Joan Serrà
2017 arXiv   pre-print
The majority of them tackle a limited number of noise conditions and rely on first-order statistics.  ...  In this work, we propose the use of generative adversarial networks for speech enhancement.  ...  We structure the training examples in two pairs (Fig. 3) : the real pair, composed of a noisy signal and a clean signal (x and x), and the fake pair, composed of a noisy signal and an enhanced signal  ... 
arXiv:1703.09452v3 fatcat:5pe245bflnbdxgtstuybsclf7u

Standard and averaging reinforcement learning in XCS

Pier Luca Lanzi, Daniele Loiacono
2006 Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06  
This paper investigates reinforcement learning (RL) in XCS.  ...  It is noted that the use of averaging RL in XCS introduces an intrinsic preference toward classifiers with a smaller fitness in the niche.  ...  The study of the relations between learning classifier systems and reinforcement learning have been widely studied.  ... 
doi:10.1145/1143997.1144241 dblp:conf/gecco/LanziL06 fatcat:whqmkq4idvgtrlplzadqsqpjcy

Exchange-correlation energy for the three-dimensional homogeneous electron gas at arbitrary temperature

Ethan W. Brown, Jonathan L. DuBois, Markus Holzmann, David M. Ceperley
2013 Physical Review B  
In doing so, we construct a Padé approximant which collapses to Debye-Hückel theory in the high-temperature, low-density limit.  ...  ^-1 < 40 and Θ = T/T_F > 0.0625).  ...  Recently we have learned that another group has performed a QMC study of the zero-temperature, spinpolarized 3D HEG. 47 Nevertheless, as noted previously, our choice of the Perdew-Zunger 30 ground state  ... 
doi:10.1103/physrevb.88.081102 fatcat:hulitcjf7zaeni7k2qswmcgxl4

A methodology for learning, analyzing, and mitigating social influence bias in recommender systems

Sanjay Krishnan, Jay Patel, Michael J. Franklin, Ken Goldberg
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
We propose a methodology to 1) learn, 2) analyze, and 3) mitigate the effect of SIB in recommender systems.  ...  In the Learning phase, we build a baseline dataset by allowing users to rate twice: before and after seeing the average rating.  ...  This work is supported in part by NSF CISE Expeditions Award CCF-1139158, LBNL Award 7076018, and DARPA XData Award  ... 
doi:10.1145/2645710.2645740 dblp:conf/recsys/KrishnanPFG14 fatcat:6p6c5x3gozgtzmkar4nbuumqiq

Patch Complexity, Finite Pixel Correlations and Optimal Denoising [chapter]

Anat Levin, Boaz Nadler, Fredo Durand, William T. Freeman
2012 Lecture Notes in Computer Science  
First, in light of computational constraints, we study the relation between denoising gain and sample size requirements in a non parametric approach.  ...  Second, we study absolute denoising limits, regardless of the algorithm used, and the converge rate to them as a function of patch size.  ...  Acknowladgments: We thank ISF, BSF, ERC, Intel, Quanta and NSF for funding.  ... 
doi:10.1007/978-3-642-33715-4_6 fatcat:f4tiqivg7nfcrieu4svwgczrm4

Natural image denoising: Optimality and inherent bounds

Anat Levin, Boaz Nadler
2011 CVPR 2011  
To overcome the absence of accurate image priors, this paper takes a non parametric approach and represents the distribution of natural images using a huge set of 10 10 patches.  ...  The problem has been studied intensively with considerable progress made in recent years.  ...  Our approach builds on the success of recent large image databases approaches in high level vision and graphics applications [11, 22] , and represents the distribution of natural images in a non-parametric  ... 
doi:10.1109/cvpr.2011.5995309 dblp:conf/cvpr/LevinN11 fatcat:cch6bhbi7bbntlubygieuhho34
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