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A Study of Structural and Parametric Learning in XCS
2006
Evolutionary Computation
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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
2007
2007 IEEE Congress on Evolutionary Computation
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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
2008
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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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]
2019
arXiv
pre-print
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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
2013
Evolutionary Intelligence
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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
2012
Evolutionary Intelligence
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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
2020
Scientific Reports
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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]
2017
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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
2006
Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06
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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
2013
Physical Review B
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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
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
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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]
2012
Lecture Notes in Computer Science
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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
2011
CVPR 2011
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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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