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Artificial Neural Networks and Machine Learning – ICANN 2012

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Cover of 'Artificial Neural Networks and Machine Learning – ICANN 2012'

Table of Contents

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    Book Overview
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    Chapter 1 Complex-Valued Multilayer Perceptron Search Utilizing Eigen Vector Descent and Reducibility Mapping
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    Chapter 2 Theoretical Analysis of Function of Derivative Term in On-Line Gradient Descent Learning
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    Chapter 3 Some Comparisons of Networks with Radial and Kernel Units
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    Chapter 4 Multilayer Perceptron for Label Ranking
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    Chapter 5 Electricity Load Forecasting: A Weekday-Based Approach
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    Chapter 6 Adaptive Exploration Using Stochastic Neurons
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    Chapter 7 Comparison of Long-Term Adaptivity for Neural Networks
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    Chapter 8 Simplifying ConvNets for Fast Learning
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    Chapter 9 A Modified Artificial Fish Swarm Algorithm for the Optimization of Extreme Learning Machines
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    Chapter 10 Retracted: Robust Training of Feedforward Neural Networks Using Combined Online/Batch Quasi-Newton Techniques
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    Chapter 11 Estimating a Causal Order among Groups of Variables in Linear Models
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    Chapter 12 Training Restricted Boltzmann Machines with Multi-tempering: Harnessing Parallelization
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    Chapter 13 A Computational Geometry Approach for Pareto-Optimal Selection of Neural Networks
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    Chapter 14 Learning Parameters of Linear Models in Compressed Parameter Space
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    Chapter 15 Control of a Free-Falling Cat by Policy-Based Reinforcement Learning
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    Chapter 16 Gated Boltzmann Machine in Texture Modeling
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    Chapter 17 Neural PCA and Maximum Likelihood Hebbian Learning on the GPU
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    Chapter 18 Construction of Emerging Markets Exchange Traded Funds Using Multiobjective Particle Swarm Optimisation
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    Chapter 19 The Influence of Supervised Clustering for RBFNN Centers Definition: A Comparative Study
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    Chapter 20 Nested Sequential Minimal Optimization for Support Vector Machines
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    Chapter 21 Random Subspace Method and Genetic Algorithm Applied to a LS-SVM Ensemble
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    Chapter 22 Text Recognition in Videos Using a Recurrent Connectionist Approach
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    Chapter 23 An Investigation of Ensemble Systems Applied to Encrypted and Cancellable Biometric Data
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    Chapter 24 New Dynamic Classifiers Selection Approach for Handwritten Recognition
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    Chapter 25 Vector Perceptron Learning Algorithm Using Linear Programming
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    Chapter 26 A Robust Objective Function of Joint Approximate Diagonalization
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    Chapter 27 TrueSkill-Based Pairwise Coupling for Multi-class Classification
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    Chapter 28 Analogical Inferences in the Family Trees Task: A Review
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    Chapter 29 An Efficient Way of Combining SVMs for Handwritten Digit Recognition
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    Chapter 30 Comparative Evaluation of Regression Methods for 3D-2D Image Registration
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    Chapter 31 A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition
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    Chapter 32 Extraction of Prototype-Based Threshold Rules Using Neural Training Procedure
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    Chapter 33 Instance Selection with Neural Networks for Regression Problems
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    Chapter 34 A New Distance for Probability Measures Based on the Estimation of Level Sets
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    Chapter 35 Low Complexity Proto-Value Function Learning from Sensory Observations with Incremental Slow Feature Analysis
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    Chapter 36 Improving Neural Networks Classification through Chaining
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    Chapter 37 Feature Ranking Methods Used for Selection of Prototypes
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    Chapter 38 A “Learning from Models” Cognitive Fault Diagnosis System
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    Chapter 39 Improving ANNs Performance on Unbalanced Data with an AUC-Based Learning Algorithm
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    Chapter 40 Learning Using Privileged Information in Prototype Based Models
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    Chapter 41 A Sparse Support Vector Machine Classifier with Nonparametric Discriminants
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    Chapter 42 Training Mahalanobis Kernels by Linear Programming
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    Chapter 43 Correntropy-Based Document Clustering via Nonnegative Matrix Factorization
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    Chapter 44 SOMM – Self-Organized Manifold Mapping
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    Chapter 45 Self-Organizing Map and Tree Topology for Graph Summarization
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    Chapter 46 Variable-Sized Kohonen Feature Map Probabilistic Associative Memory
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    Chapter 47 Learning Deep Belief Networks from Non-stationary Streams
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    Chapter 48 Separation and Unification of Individuality and Collectivity and Its Application to Explicit Class Structure in Self-Organizing Maps
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    Chapter 49 Autoencoding Ground Motion Data for Visualisation
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    Chapter 50 Examining an Evaluation Mechanism of Metaphor Generation with Experiments and Computational Model Simulation
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    Chapter 51 Pairwise Clustering with t-PLSI
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    Chapter 52 Selecting β -Divergence for Nonnegative Matrix Factorization by Score Matching
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    Chapter 53 Neural Networks for Proof-Pattern Recognition
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    Chapter 54 Using Weighted Clustering and Symbolic Data to Evaluate Institutes’s Scientific Production
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    Chapter 55 Comparison of Input Data Compression Methods in Neural Network Solution of Inverse Problem in Laser Raman Spectroscopy of Natural Waters
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    Chapter 56 New Approach for Clustering Relational Data Based on Relationship and Attribute Information
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    Chapter 57 Comparative Study on Information Theoretic Clustering and Classical Clustering Algorithms
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    Chapter 58 Text Mining for Wellbeing: Selecting Stories Using Semantic and Pragmatic Features
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    Chapter 59 Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts
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    Chapter 60 Estimating Quantities: Comparing Simple Heuristics and Machine Learning Algorithms
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    Chapter 61 Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets
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    Chapter 62 Using a Support Vector Machine and Sampling to Classify Compounds as Potential Transdermal Enhancers
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    Chapter 63 The Application of Gaussian Processes in the Predictions of Permeability across Mammalian Membranes
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    Chapter 64 Protein Structural Blocks Representation and Search through Unsupervised NN
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    Chapter 65 Evolutionary Support Vector Machines for Time Series Forecasting
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    Chapter 66 Learning Relevant Time Points for Time-Series Data in the Life Sciences
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    Chapter 67 A Multivariate Approach to Estimate Complexity of FMRI Time Series
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    Chapter 68 Neural Architectures for Global Solar Irradiation and Air Temperature Prediction
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    Chapter 69 Sparse Linear Wind Farm Energy Forecast
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    Chapter 70 Diffusion Maps and Local Models for Wind Power Prediction
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    Chapter 71 A Hybrid Model for S&P500 Index Forecasting
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    Chapter 72 Erratum: Robust Training of Feedforward Neural Networks Using Combined Online/Batch Quasi-Newton Techniques
Overall attention for this book and its chapters
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Title
Artificial Neural Networks and Machine Learning – ICANN 2012
Published by
Springer Berlin Heidelberg, September 2012
DOI 10.1007/978-3-642-33266-1
ISBNs
978-3-64-233265-4, 978-3-64-233266-1
Editors

Villa, Alessandro E. P., Duch, Włodzisław, Érdi, Péter, Masulli, Francesco, Palm, Günther

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 2%
Student > Bachelor 2 2%
Student > Master 2 2%
Researcher 2 2%
Student > Postgraduate 2 2%
Other 0 0%
Unknown 74 88%
Readers by discipline Count As %
Computer Science 7 8%
Engineering 2 2%
Earth and Planetary Sciences 1 1%
Unknown 74 88%