UID:
almafu_9960950840502883
Format:
1 online resource (1672 p.)
ISBN:
0-262-30949-1
,
1-282-09674-5
,
0-262-25691-6
Series Statement:
Advances in neural information processing systems,
Content:
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.
Note:
Description based upon print version of record.
,
An Application of Reinforcement Learning to Aerobatic Helicopter Flight; Tighter PAC-Bayes Bounds; Online Classification for Complex Problems Using Simultaneous Projections; Learning on Graph with Laplacian Regularization; Multi-Task Feature Learning; Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning; Efficient Methods for Privacy Preserving Face Detection; Active learning for misspecified generalized linear models; Subordinate class recognition using relational object models
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Unified Inference for Variational Bayesian Linear Gaussian State-Space Models; A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems; Sample complexity of policy search with known dynamics; AdaBoost is Consistent; A selective attention multi-chip system with dynamic synapses and spiking neurons; Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks; Convergence of Laplacian Eigenmaps; Analysis of Representations for Domain Adaptation; An Approach to Bounded Rationality; Greedy Layer-Wise Training of Deep Networks
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Dirichlet-Enhanced Spam Filtering based on Biased Samples; Detecting Humans via Their Pose; Similarity by Composition; Denoising and Dimension Reduction in Feature Space; Learning to Rank with Nonsmooth Cost Functions; Conditional mean field; Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation; Branch and Bound for Semi-Supervised Support Vector Machines; Automated Hierarchy Discovery for Planning in Partially Observable Environments; Max-margin classification of incomplete data; Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model
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Implicit Online Learning with Kernels; Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons; Bayesian Ensemble Learning; Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions; Map-Reduce for Machine Learning on Multicore; Relational Learning with Gaussian Processes; Recursive Attribute Factoring; On Transductive Regression; Balanced Graph Matching; Learning from Multiple Sources; Kernels on Structured Objects Through Nested Histograms; Differential Entropic Clustering of Multivariate Gaussians
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Support Vector Machines on a Budget; A Theory of Retinal Population Coding; Learning to Traverse Image Manifolds; Using Combinatorial Optimization within Max-Product Belief Propagation; Optimal Single-Class Classification Strategies; A Small World Threshold for Economic Network Formation; PG-means: learning the number of clusters in data; Clustering Under Prior Knowledge with Application to Image Segmentation; Multi-dynamic Bayesian Networks; Image Retrieval and Classification Using Local Distance Functions; Multiple Instance Learning for Computer Aided Diagnosis
,
Distributed Inference in Dynamical Systems
,
Also available in print.
,
English
Additional Edition:
ISBN 0-262-19568-2
Language:
English
URL:
OCLC metadata license agreement
