Advances in Neural Information Processing Systems 19

Proceedings of the 2006 Conference
MIT Press
  • 1. Auflage
  • |
  • erschienen am 11. September 2017
  • |
  • 1672 Seiten
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-262-25691-9 (ISBN)
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists -- interested in theoretical and applied aspects of modeling, simulating, and building neural-like or intelligent systems. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning, and applications. Only twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.
  • Englisch
  • Cambridge
  • |
  • USA
978-0-262-25691-9 (9780262256919)
0262256916 (0262256916)
weitere Ausgaben werden ermittelt
  • Intro
  • Contents
  • Preface
  • Donors
  • NIPS Foundation
  • Committees
  • Reviewers
  • 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
  • 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
  • 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
  • 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
  • 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
  • iLSTD: Eligibility Traces and Convergence Analysis
  • A PAC-Bayes Risk Bound for General Loss Functions
  • Bayesian Policy Gradient Algorithms
  • Data Integration for Classification Problems Employing Gaussian Process Priors
  • Approximate inference using planar graph decomposition
  • Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints
  • No-regret Algorithms for Online Convex Programs
  • Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis
  • Approximate Correspondences in High Dimensions
  • A Kernel Method for the Two-Sample-Problem
  • Learning Nonparametric Models for Probabilistic Imitation
  • Training Conditional Random Fields for Maximum Labelwise Accuracy
  • Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces
  • Graph-Based Visual Saliency
  • Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
  • Manifold Denoising
  • TrueSkill: A Bayesian Skill Rating System
  • Prediction on a Graph with a Perceptron
  • Geometric entropy minimization (GEM) for anomaly detection and localization
  • Single Channel Speech Separation Using Factorial Dynamics
  • Correcting Sample Selection Bias by Unlabeled Data
  • Sparse Representation for Signal Classification
  • In-N etwork PCA and Anomaly Detection
  • Learning Time-Intensity Proxles of Human Activity using Non-Parametric Bayesian Models
  • Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm
  • Adaptor Grammars:A Framework for Specifying Compositional Nonparametric Bayesian Models
  • A Humanlike Predictor of Facial Attractiveness
  • Clustering appearance and shape by learning jigsaws
  • A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
  • An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
  • Combining casual and similarity-based reasoning
  • A Nonparametric Approach to Bottom-Up Visual Saliency
  • Hierarchical Dirichlet Processes with Random Effects
  • An Information Theoretic Framework for Eukaryotic Gradient Sensing
  • Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons
  • Predicting spike times from subthreshold dynamics of a neuron
  • Gaussian and Wishart Hyperkernels
  • Causal inference in sensorimotor integration
  • Multiple timescales and uncertainty in motor adaptation
  • Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach
  • Accelerated Variational Dirichlet Process Mixtures
  • PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
  • Inducing Metric Violations in Human Similarity Judgements
  • Modelling transcriptional regulation using Gaussian processes
  • Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields
  • Efficient sparse coding algorithms
  • A Bayesian Approach to Diffusion Models of Decision-Making and Response Time
  • Efficient Structure Learning of Markov Networks using L1-Regularization
  • Aggregating Classification Accuracy across Time: Application to Single Trial EEG
  • Uncertainty,phase and oscillatory hippocampal recall
  • Blind Motion Deblurring Using Image Statistics
  • Speakers optimize information density through syntactic reduction
  • Real-time adaptive information-theoretic optimization of neurophysiology experiments
  • Ordinal Regression by Extended Binary Classification
  • Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data
  • Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space
  • Learnability and the Doubling Dimension
  • Emergence of conjunctive visual features by quadratic independent component analysis
  • Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure
  • Analysis of Contour Motions
  • Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions
  • Dynamic Foreground/Background Extraction from Images and Videos using Random Patches
  • Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning
  • Statistical Modeling of Images with Fields of Gaussian Scale Mixtures
  • An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
  • Isotonic Conditional Random Fields and Local Sentiment Flow
  • Part-based Probabilistic Point Matching using Equivalence Constraints
  • Modeling Dyadic Data with Binary Latent Factors
  • Fast Discriminative Visual Codebooks using Randomized Clustering Forests
  • Context Effects in Category Learning: An Investigation of Four Probabilistic Models
  • Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games
  • N on-rigid point set registration: Coherent Point Drift
  • Fundamental Limitations of Spectral Clustering
  • On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts
  • A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
  • Temporal dynamics of information content carried by neurons in the primary visual cortex
  • Blind source separation for over-determined delayed mixtures
  • The Neurodynamics of Belief Propagation on Binary Markov Random Fields
  • Handling Advertisements of Unknown Quality in Search Advertising
  • Bayesian Model Scoring in Markov Random Fields
  • Game theoretic algorithms for Protein-DNA binding
  • Bayesian Image Super-resolution, Continued
  • Parameter Expanded Variational Bayesian Methods
  • Inferring Network Structure from Co-Occurrences
  • Unsupervised Regression with Applications to Nonlinear System Identification
  • Stability of K-Means Clustering
  • Learning to parse images of articulated bodies
  • Efficient Learning of Sparse Representations with an Energy-Based Model
  • Learning to be Bayesian without Supervision
  • Boosting Structured Prediction for Imitation Learning
  • Large Scale Hidden Semi-Markov SVMs
  • Natural Actor-Critic for Road Traffic Optimisation
  • Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
  • Learning annotated hierarchies from relational data
  • Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
  • Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation
  • Robotic Grasping of Novel Objects
  • Theory and Dynamics of Perceptual Bistability
  • Fast Iterative Kernel PCA
  • Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
  • Information Bottleneck for Non Co-Occurrence Data
  • Large Margin Hidden Markov Models for Automatic Speech Recognition
  • Nonlinear physically-based models for decoding motor-cortical population activity
  • Convex Repeated Games and Fenchel Duality
  • Recursive ICA
  • Chained Boosting
  • A recipe for optimizing a time-histogram Hideaki Shimazaki
  • Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
  • Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space
  • Learning Dense 3D Correspondence
  • An Oracle Inequality for Clipped Regularized Risk Minimizers
  • Learning Structural Equation Models for fMRI
  • Mixture Regression for Covariate Shift
  • Modeling Human Motion Using Binary Latent Variables
  • A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
  • Towards a general independent subspace analysis
  • Linearly-solvable Markov decision problems
  • Logistic Regression for Single Trial EEG Classification
  • Large Margin Component Analysis
  • Learning Motion Style Synthesis from Perceptual Observations
  • Large-Scale Sparsified Manifold Regularization
  • Scalable Discriminative Learning for Natural Language Parsing and Translation
  • Generalized Maximum Margin Clustering and Unsupervised Kernel Learning
  • A Complexity-Distortion Approach to Joint Pattern Alignment
  • Online Clustering of Moving Hyperplanes
  • Comparative Gene Prediction using Conditional Random Fields
  • Fast Computation of Graph Kernels
  • Temporal Coding using the Response Properties of Spiking Neurons
  • High-Dimensional Graphical Model Selection Using l1 -Regularized Logistic Regression
  • Attentional Processing on a Spike-Based VLSI Neural Network
  • Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
  • Graph Laplacian Regularization for Large-Scale Semidefinite Programming
  • A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo
  • Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization
  • Particle Filtering for Nonparametric Bayesian Matrix Factorization
  • A Scalable Machine Learning Approach to Go
  • A Local Learning Approach for Clustering
  • The Robustness-Performance Tradeoff in Markov Decision Processes
  • Optimal Change-Detection and Spiking Neurons
  • Stochastic Relational Models for Discriminative Link Prediction
  • Nonnegative Sparse PCA
  • Doubly Stochastic Normalization for Spectral Clustering
  • Simplifying Mixture Models through Function Approximation
  • Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms
  • MLLE: Modified Locally Linear Embedding Using Multiple Weights
  • Learning with Hypergraphs:Clustering, Classification, and Embedding
  • Multi-Instance Multi-Label Learning with Application to Scene Classification
  • Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing
  • A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG Data
  • Subject Index
  • Author Index

Dateiformat: PDF
Kopierschutz: Adobe-DRM (Digital Rights Management)


Computer (Windows; MacOS X; Linux): Installieren Sie bereits vor dem Download die kostenlose Software Adobe Digital Editions (siehe E-Book Hilfe).

Tablet/Smartphone (Android; iOS): Installieren Sie bereits vor dem Download die kostenlose App Adobe Digital Editions (siehe E-Book Hilfe).

E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nicht Kindle)

Das Dateiformat PDF zeigt auf jeder Hardware eine Buchseite stets identisch an. Daher ist eine PDF auch für ein komplexes Layout geeignet, wie es bei Lehr- und Fachbüchern verwendet wird (Bilder, Tabellen, Spalten, Fußnoten). Bei kleinen Displays von E-Readern oder Smartphones sind PDF leider eher nervig, weil zu viel Scrollen notwendig ist. Mit Adobe-DRM wird hier ein "harter" Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.

Weitere Informationen finden Sie in unserer E-Book Hilfe.

Download (sofort verfügbar)

197,09 €
inkl. 19% MwSt.
Download / Einzel-Lizenz
PDF mit Adobe DRM
siehe Systemvoraussetzungen
E-Book bestellen

Unsere Web-Seiten verwenden Cookies. Mit der Nutzung dieser Web-Seiten erklären Sie sich damit einverstanden. Mehr Informationen finden Sie in unserem Datenschutzhinweis. Ok