NECO 2006 - PDF Preprint Papers

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NECO
2006

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Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression

14 years 12 days ago

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The application of boosting technique to the regression problems has received relatively little attention in contrast to the research aimed at classification problems. This paper ...

Durga L. Shrestha, Dimitri P. Solomatine

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Id	Views	Title	Status

1	157	Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression	claim
2	118	Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines	claim
3	111	Dynamics and Topographic Organization of Recursive Self-Organizing Maps	claim
4	103	Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning	claim
5	98	On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields	claim
6	98	Comment on "Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes, " by M. Rudolph and A. Destexhe	claim
7	97	Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation	claim
8	86	Modeling Sensorimotor Learning with Linear Dynamical Systems	claim
9	86	Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images	claim
10	86	A Comparison of Descriptive Models of a Single Spike Train by Information-Geometric Measure	claim
11	86	Bifurcation Analysis of Jansen's Neural Mass Model	claim
12	82	The Costs of Ignoring High-Order Correlations in Populations of Model Neurons	claim
13	81	Sensory Memory for Odors Is Encoded in Spontaneous Correlated Activity Between Olfactory Glomeruli	claim
14	80	Short-Term Synaptic Plasticity Can Enhance Weak Signal Detectability in Nonrenewal Spike Trains	claim
15	76	What Is the Relation Between Slow Feature Analysis and Independent Component Analysis?	claim
16	76	Spontaneous Dynamics of Asymmetric Random Recurrent Spiking Neural Networks	claim
17	72	Facial Attractiveness: Beauty and the Machine	claim
18	72	Simultaneous Rate-Synchrony Codes in Populations of Spiking Neurons	claim
19	72	How Inhibitory Oscillations Can Train Neural Networks and Punish Competitors	claim
20	71	Low-Dimensional Maps Encoding Dynamics in Entorhinal Cortex and Hippocampus	claim
21	69	Analysis of Synchronization Between Two Modules of Pulse Neural Networks with Excitatory and Inhibitory Connections	claim
22	69	Topographic Product Models Applied to Natural Scene Statistics	claim
23	69	Programmable Logic Construction Kits for Hyper-Real-Time Neuronal Modeling	claim
24	68	A Fast Learning Algorithm for Deep Belief Nets	claim
25	67	Estimating Spiking Irregularities Under Changing Environments	claim
26	64	Implications of Neuronal Diversity on Population Coding	claim
27	62	Payoff-Monotonic Game Dynamics and the Maximum Clique Problem	claim
28	62	Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics	claim
29	61	Recognition by Variance: Learning Rules for Spatiotemporal Patterns	claim
30	61	A State-Space Analysis for Reconstruction of Goal-Directed Movements Using Neural Signals	claim
31	60	Representation and Timing in Theories of the Dopamine System	claim
32	60	The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx	claim
33	60	On the Computational Power of Threshold Circuits with Sparse Activity	claim
34	58	Correlation and Independence in the Neural Code	claim
35	58	Response Variability in Balanced Cortical Networks	claim
36	58	A Sensorimotor Map: Modulating Lateral Interactions for Anticipation and Planning	claim
37	57	Parameter Space Structure of Continuous-Time Recurrent Neural Networks	claim
38	56	Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter	claim
39	55	Images, Frames, and Connectionist Hierarchies	claim
40	54	Effects of Fast Presynaptic Noise in Attractor Neural Networks	claim
41	54	Spatiotemporal Structure in Large Neuronal Networks Detected from Cross-Correlation	claim
42	52	The Optimal Synapse for Sparse, Binary Signals in the Rod Pathway	claim
43	51	Enhancing Density-Based Data Reduction Using Entropy	claim
44	51	A Unifying View of Wiener and Volterra Theory and Polynomial Kernel Regression	claim
45	50	Properties of Synergies Arising from a Theory of Optimal Motor Behavior	claim
46	50	Memory Capacity for Sequences in a Recurrent Network with Biological Constraints	claim
47	49	Polychronization: Computation with Spikes	claim
48	48	Exact Simulation of Integrate-and-Fire Models with Synaptic Conductances	claim
49	48	Receptive Field Structures for Recognition	claim
50	48	Optimal Neuronal Tuning for Finite Stimulus Spaces	claim
51	48	How Noise Affects the Synchronization Properties of Recurrent Networks of Inhibitory Neurons	claim
52	48	Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors	claim
53	47	A Maximum Likelihood Approach to Density Estimation with Semidefinite Programming	claim
54	47	The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise	claim
55	41	An Oscillatory Neural Model of Multiple Object Tracking	claim

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