IJON 2010 - PDF Preprint Papers

IJON 2010

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IJON
2010

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DFS-generated pathways in GA crossover for protein structure prediction

13 years 2 months ago

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Genetic Algorithms (GAs), as nondeterministic conformational search techniques, are promising for solving protein structure prediction (PSP) problems. The crossover operator of a ...

Tamjidul Hoque, Madhu Chetty, Andrew Lewis, Abdul ...

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Id	Views	Title	Status

1	199	DFS-generated pathways in GA crossover for protein structure prediction	claim
2	189	Inference and parameter estimation on hierarchical belief networks for image segmentation	claim
3	184	Human action recognition using extreme learning machine based on visual vocabularies	claim
4	181	Active learning with extremely sparse labeled examples	claim
5	178	An empirical study of two typical locality preserving linear discriminant analysis methods	claim
6	159	Model predictive flight control using adaptive support vector regression	claim
7	153	Feature selection for time series prediction - A combined filter and wrapper approach for neural networks	claim
8	152	Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data	claim
9	150	Linear discriminant analysis using rotational invariant L1 norm	claim
10	148	Modeling radiation-induced lung injury risk with an ensemble of support vector machines	claim
11	148	Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines	claim
12	140	Multi-task preference learning with an application to hearing aid personalization	claim
13	139	Event monitoring via local motion abnormality detection in non-linear subspace	claim
14	139	Multilinear principal component analysis for face recognition with fewer features	claim
15	138	A dynamic Bayesian network to represent discrete duration models	claim
16	129	Robust object tracking using a spatial pyramid heat kernel structural information representation	claim
17	127	Oscillation in a network model of neocortex	claim
18	121	Sample-dependent graph construction with application to dimensionality reduction	claim
19	121	Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm	claim
20	120	Semi-supervised learning with varifold Laplacians	claim
21	119	Hyperparameter learning in probabilistic prototype-based models	claim
22	119	V-Proportion: A method based on the Voronoi diagram to study spatial relations in neuronal mosaics of the retina	claim
23	114	Optimization method based extreme learning machine for classification	claim
24	114	Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion	claim
25	112	Efficient voting prediction for pairwise multilabel classification	claim
26	110	Large margin classifiers based on affine hulls	claim
27	109	Supervised learning of local projection kernels	claim
28	109	Variational inference for Student-t MLP models	claim
29	107	Multiclass support vector classification via coding and regression	claim
30	105	Regression based D-optimality experimental design for sparse kernel density estimation	claim
31	104	Feature extraction based on fuzzy 2DLDA	claim
32	103	Improving liquid state machines through iterative refinement of the reservoir	claim
33	100	Automatic detection of onset and cessation of tree stem radius increase using dendrometer data	claim
34	99	Feature evaluation and selection based on neighborhood soft margin	claim
35	98	A unified distance measurement for orientation coding in palmprint verification	claim
36	98	Maximum margin criterion with tensor representation	claim
37	97	Asymptotic tracking control scheme for mechanical systems with external disturbances and friction	claim
38	97	Processing with cell assemblies	claim
39	94	Optimizing an organized modularity measure for topographic graph clustering: A deterministic annealing approach	claim
40	94	Constrained Laplacian Eigenmap for dimensionality reduction	claim
41	93	Discriminant analysis via support vectors	claim
42	91	Bayesian networks	claim
43	91	Multiple fuzzy neural networks modeling with sparse data	claim
44	85	A hidden process regression model for functional data description. Application to curve discrimination	claim

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