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ESANN 2006

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ESANN
2006

599views Neural Networks» more ESANN 2006»

Learning Visual Invariance

14 years 1 months ago

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Invariance is a necessary feature of a visual system able to recognize real objects in all their possible appearance. It is also the processing step most problematic to understand ...

Alessio Plebe

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Id	Views	Title	Status

1	599	Learning Visual Invariance	claim
2	262	Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms	claim
3	236	Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networks	claim
4	202	LS-SVM functional network for time series prediction	claim
5	202	Data topology visualization for the Self-Organizing Map	claim
6	199	Recognition of handwritten digits using sparse codes generated by local feature extraction methods	claim
7	192	Margin based Active Learning for LVQ Networks	claim
8	187	Visualizing gene interaction graphs with local multidimensional scaling	claim
9	186	Bayesian source separation: beyond PCA and ICA	claim
10	182	Adaptive Sensor Modelling and Classification using a Continuous Restricted Boltzmann Machine (CRBM)	claim
11	179	Visual nonlinear discriminant analysis for classifier design	claim
12	170	Discriminacy of the minimum range approach to blind separation of bounded sources	claim
13	164	A Gaussian process latent variable model formulation of canonical correlation analysis	claim
14	161	OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method	claim
15	158	Dynamical reservoir properties as network effects	claim
16	157	Data mining techniques for feature selection in blood cell recognition	claim
17	152	Hierarchical markovian models for joint classification, segmentation and data reduction of hyperspectral images	claim
18	151	Classification of Boar Sperm Head Images using Learning Vector Quantization	claim
19	149	Lag selection for regression models using high-dimensional mutual information	claim
20	148	Using distributed genetic programming to evolve classifiers for a brain computer interface	claim
21	144	Evolino for recurrent support vector machines	claim
22	141	FastISA: A fast fixed-point algorithm for independent subspace analysis	claim
23	141	Hierarchical analysis of GSM network performance data	claim
24	140	Magnification control for batch neural gas	claim
25	138	On the selection of hidden neurons with heuristic search strategies for approximation	claim
26	137	Neural networks and machine learning in bioinformatics - theory and applications	claim
27	135	Diversity creation in local search for the evolution of neural network ensembles	claim
28	134	Parallel hardware implementation of a broad class of spiking neurons using serial arithmetic	claim
29	133	Bootstrap feature selection in support vector machines for ventricular fibrillation detection	claim
30	130	Classification by means of Evolutionary Response Surfaces	claim
31	130	Cluster detection algorithm in neural networks	claim
32	129	Iterative context compilation for visual object recognition	claim
33	128	Visual object classification by sparse convolutional neural networks	claim
34	126	FPGA implementation of an integrate-and-fire LEGION model for image segmentation	claim
35	126	Connection strategies in neocortical networks	claim
36	124	Weighted differential topographic function: a refinement of topographic function	claim
37	123	Linking non-binned spike train kernels to several existing spike train metrics	claim
38	121	Independent dynamics subspace analysis	claim
39	121	Non-orthogonal Support Width ICA	claim
40	121	Learning and discrimination through STDP in a top-down modulated associative memory	claim
41	120	Optimal design of hierarchical wavelet networks for time-series forecasting	claim
42	119	Determination of the Mahalanobis matrix using nonparametric noise estimations	claim
43	119	EM-algorithm for training of state-space models with application to time series prediction	claim
44	119	Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system	claim
45	118	Semi-Blind Approaches for Source Separation and Independent component Analysis	claim
46	118	Fuzzy image segmentation with Fuzzy Labelled Neural Gas	claim
47	118	Variants of Unsupervised Kernel Regression: General cost functions	claim
48	117	Extended model of conditioned learning within latent inhibition	claim
49	116	Saliency extraction with a distributed spiking neural network	claim
50	116	Outlier identification with the Harmonic Topographic Mapping	claim
51	116	Visual Data Mining and Machine Learning	claim
52	116	Nonlinear dynamics in neural computation	claim
53	115	Construction of a memory management system in an on-line learning mechanism	claim
54	115	Learning for stochastic dynamic programming	claim
55	114	Reducing policy degradation in neuro-dynamic programming	claim
56	114	Non-linear gating network for the large scale classification model CombNET-II	claim
57	114	An algorithm for fast and reliable ESOM learning	claim
58	111	A Cyclostationary Neural Network model for the prediction of the NO2 concentration	claim
59	111	Efficient Forward Regression with Marginal Likelihood	claim
60	110	Cultures of dissociated neurons display a variety of avalanche behaviours	claim
61	110	Designing neural network committees by combining boosting ensembles	claim
62	109	Sanger-driven MDSLocalize - a comparative study for genomic data	claim
63	108	Immune Network based Ensembles	claim
64	108	Degeneracy in model selection for SVMs with radial Gaussian kernel	claim
65	108	Rotation-based ensembles of RBF networks	claim
66	107	Time series prediction using DirRec strategy	claim
67	106	Using sampling methods to improve binding site predictions	claim
68	105	Evolving multi-segment 'super-lamprey' CPG's for increased swimming control	claim
69	105	A simple idea to separate convolutive mixtures in an undetermined scenario	claim
70	103	Topological Correlation	claim
71	103	Entropy-based principle and generalized contingency tables	claim
72	101	Gaussian and exponential architectures in small-world associative memories	claim
73	99	Nonlinear transient computation and variable noise tolerance	claim
74	99	Unsupervised clustering of continuous trajectories of kinematic trees with SOM-SD	claim
75	97	Generalization properties of spiking neurons trained with ReSuMe method	claim
76	96	Enhanced maxcut clustering with multivalued neural networks and functional annealing	claim
77	96	Selection of more than one gene at a time for cancer prediction from gene expression data	claim
78	95	The permutation test for feature selection by mutual information	claim
79	94	On-line adaptation of neuro-prostheses with neuronal evaluation signals	claim
80	94	Consistent estimation of the architecture of multilayer perceptrons	claim
81	91	Modelling switching dynamics using prediction experts operating on distinct wavelet scales	claim
82	91	Stochastic Processes for Canonical Correlation Analysis	claim
83	90	Learning what is important: feature selection and rule extraction in a virtual course	claim
84	88	Robust Local Cluster Neural Networks	claim
85	85	Synthesis of maximum margin and multiview learning using unlabeled data	claim
86	84	Spatial filters for the classification of event-related potentials	claim
87	72	A multiagent architecture for concurrent reinforcement learning	claim

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