NDLI: Probabilistic principal surfaces

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2015 International Joint Conference on Neural Networks (IJCNN)

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2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)

Proceedings of the International Joint Conference on Neural Networks, 2003.

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

Volume 6 - 1999

Volume 5 - 1999

Volume 4 - 1999

Volume 3 - 1999

Volume 2 - 1999

Table of contents

A self-organizing network system forming memory from nonstationary probability distributions

Knowledge processing system using chaotic associative memory

Chaotic associative memory for sequential patterns

A new bidirectional associative memory based on distributed representation

A combined multi-Winner multidirectional associative memory

Comparison between theory and simulation for the two-level decoupled Hamming associative memory

Enhancing the storage capacity of binary associative memories

Capacity of second-ordered bidirectional associative memory in the presence of noise bits

Determining field of visual attention in associative memories

An architecture for an adaptive associative memory system based on autonomous reactions between images

Behavior of interactive neural networks as associative memories

A learning method for synthesizing associative memory in neural networks

Partitioned architectures for large scale data recovery

Priority ordered architecture of neural networks

Boosting the performance of weightless neural networks by using a postprocessing transformation of the output scores

Approximation of chaotic shapes with tree-structured neural networks

Genetic optimization of NN topologies for the task of natural language processing

Nonlinear channel equalization using new neural network model

Algorithmically universal model of structureless parallelism

Performance improvement of the BSB model in classifier tasks using optimization techniques

On the conditions of outer-supervised feedforward neural networks for null cost learning

An adjustable model for linear to nonlinear regression

The modular map

Using factorial design to optimise neural networks

Perceptrons revisited: the addition of a non-monotone recursion greatly enhances their representation and classification properties

Towards a high performance neural branch predictor

A spatial quantum neural computer

Inverse PCA method for weight initialization in CMLP network

Locally linear independent component analysis

A comparison of neural ICA algorithms using real-world data

A fast algorithm for estimating overcomplete ICA bases for image windows

Multiclass least squares support vector machines

On support vector decision trees for database marketing

Loss function for blind source separation-minimum entropy criterion and its generalized anti-Hebbian rules

Optimal dimension reduction and transform coding with mixture principal components

An information theoretic method for designing multiresolution principal component transforms

MICA: multimodal independent component analysis

Learning algorithm for independent component analysis by geodesic flows on orthogonal group

A VLSI friendly algorithm for support vector machines

Moderating the outputs of support vector machine classifiers

Temporal BYY learning and its applications to extended Kalman filtering, hidden Markov model, and sensor-motor integration

An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

Autoregressive signal separation approach with seesaw-mapping technique on temporal source separation

On the study of BKYY cluster number selection criterion for small sample data set with bootstrap technique

Support vector approaches for engine knock detection

Hybrid neural networks for frequency estimation of unevenly sampled data

Adaptive blind MIMO system identification using principal component neural models

'Mechanical' neural learning and InfoMax orthonormal independent component analysis

Input variable selection using independent component analysis

Real world blind separation of convolved speech signals

Accelerated training of support vector machines

Nonlinear methods for clustering and reduction of dimensionality

Local-to-global topological methods in data analysis and applications to fMRI of human brain

A local face statistics recognition methodology beyond ICA and/or PCA

Early vision image analyses using ICA in unsupervised learning ANN

Implementing neural networks into modern technology

Blind de-mixing real-time algorithm of piecewise time series mixture

Maximum entropy ICA constrained by individual entropy maximization employing self-organizing maps

Adaptive algorithms for accelerated PCA from an augmented Lagrangian cost function

Thermodynamics proof of sensory learning and implication of mammal homeostasis

Blind separation of convolutive mixtures

Independent subspace analysis shows emergence of phase and shift invariant features from natural images

Multi-resolution support vector machine

Temporal Bayesian Ying-Yang dependence reduction, blind source separation and principal independent components

Performance of RBF equalizer in data storage channels

A game-theoretic formulation on adaptive categorization in ART networks

Self-organization of shift-invariant receptive fields

A self-scaling procedure in unsupervised correlational neural networks

Unsupervised curve-based clustering

Unsupervised context-based learning of multiple temporal sequences

Probabilistic principal surfaces

A study of parallel neural networks

Analytical results on pseudo-polynomial functional-link neural units for blind density shaping

Feature extraction from data structures with unsupervised recursive neural networks

Parallel self-organization map using multiple stimuli

Self-creating and adaptive learning of RBF networks: merging soft-competition clustering algorithm with network growth technique

A cluster analysis based on a regularization method

A density based membership function for fuzzy clustering

A novel neural network for four-term analogy based on area representation

Multi-level neural networks

Analyzing weight distribution of neural networks

The two spirals benchmark: lessons from the hidden layers

A new framework for modeling learning dynamics

Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural network

When local isn't enough: extracting distributed rules from networks

Validation of neural networks using hybrid resampling methods

Looking inside the ANN "black box": classifying individual neurons as outlier detectors

Neural networks input selection by using the training set

Dimensionality reduction using a novel neural network based feature extraction method

Visualization of radial basis function networks

Gauss-sigmoid neural network

Convergence analysis of the Quickprop method

Confidence and prediction intervals for neural network ensembles

Discretization methods for encoding of continuous input variables for Boolean neural networks

Parallel, self organizing, consensus neural networks

A neural network for learning domain rules with precision

Learning in a quantizable neural network

Powell's dogleg trust-region steps with the quasi-Newton augmented Hessian for neural nonlinear least-squares learning

Sample path-based policy-only learning by actor neural networks

Direction-basis-function neural networks

Z splitting criterion for growing trees and boosting

The learning behavior of single neuron classifiers on linearly separable or nonseparable input

RBF neural networks with centers assignment via Karhunen-Loeve transform

A comparison of Eclectic learning and Stagger

Optimal use of regularization and cross-validation in neural network modeling

Ensemble learning using observational learning theory

Ensemble learning using observational learning theory

A comparison between two interval arithmetic learning algorithms

Object oriented learning network and its applications

Universal learning networks with varying parameters

Size-reducing RBF networks

Improved mutual information feature selector for neural networks in supervised learning

Incremental class learning-an approach to longlife and scalable learning

Rival rewarded and randomly rewarded rival competitive learning

Biofunctionality: a novel learning method for intelligent agents

Efficient training techniques for classification with vast input space

Convergent design of a piecewise linear neural network

Improved generalisation using cooperative learning and rule extraction

Incremental learning using sensitivity analysis

Hebbian learning and competition in the neural abstraction pyramid

Iterative fast orthogonal search algorithm for sparse self-structuring generalized single-layer networks

The /spl alpha/-EM learning and its cookbook: from mixture-of-expert neural networks to movie random field

Non-iterative learning for neural networks

A novel fast learning algorithms for time-delay neural networks

Preconditioning method to accelerate neural networks gradient training algorithms

A new learning algorithm without explicit error backpropagation

Weighted least square ensemble networks

Mediated and multi-level information processing

Controlling simple structural information to improve generalization performance

Combining cross-validation and confidence to measure fitness

Adaptive training methods for optimal margin classification

Approximate maximum entropy joint feature inference for discrete space classification

Lotto-type competitive learning and its stability

Neural networks to estimate ML multi-class constrained conditional probability density functions

A general formulation for learning multi-class posterior probabilities

Selection of training samples for learning with hints

Examination of effectiveness of higher-order mean field Boltzmann machine learning based on linear response theorem

Efficient kernel functions for the general regression and modified probabilistic neural networks

A dynamic mapping based on probabilistic relaxation

Averaging ensembles of self-organising mixture networks for density estimation

A new method for density estimation by using forward neural network

Conditional entropy minimization in neural network classifiers

Iterative Improvement of trigonometric networks

Volume 1 - 1999

1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan)

[Proceedings 1992] IJCNN International Joint Conference on Neural Networks

[Proceedings] 1991 IEEE International Joint Conference on Neural Networks

IJCNN-91-Seattle International Joint Conference on Neural Networks

1990 IJCNN International Joint Conference on Neural Networks

International 1989 Joint Conference on Neural Networks

Probabilistic principal surfaces

Content Provider	IEEE Xplore Digital Library
Author	Kyu-Yu Chang Ghosh, J.
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA (Kyu-Yu Chang)
Abstract	A modification to the current probabilistic formulations of the principal curve and principal surface is proposed. The modification involves orienting and clipping the covariances at each of the manifold nodes such that variance in directions tangential to the manifold are minimized. The motivation behind this modification lies in the desire to recover and approximate the projection step of the original principal curve algorithm in current probabilistic principal surface formulations. Experiments on artificial and real datasets suggest that this modification does indeed lead to a vast improvement in convergence speed and better generalization properties for principal surfaces.
Starting Page	1107
Ending Page	1112
File Size	538520
Page Count	6
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.831111
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surface topography Gaussian processes Mean square error methods Equations Manifolds Convergence Data visualization Piecewise linear approximation Principal component analysis Grid computing
Content Type	Text
Resource Type	Article

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