Pattern Recognition and Neural Networks

B. D. Ripley

doi:https://doi.org/10.1017/cbo9780511812651

Pattern Recognition and Neural Networks

1996 · Cambridge University Press eBooks · 6,514 citations · 16 from inside this corpus

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

30 of 30 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Greedy function approximation: A gradient boosting machine.→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
The Elements of Statistical Learning: Data Mining, Inference, and Prediction→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Gaussian Processes for Machine Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Bayesian Network Classifiers→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Model-Based Clustering, Discriminant Analysis, and Density Estimation→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Semi-Supervised Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Support Vector Data Description→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Introduction to Bayesian Networks→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Logistic regression and artificial neural network classification models: a methodology re…→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Fundamentals of Artificial Neural Networks→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Ensemble Machine Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Approximation theory of the MLP model in neural networks→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Pattern Recognition and Machine Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Feature subset selection using a genetic algorithm→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Support-Vector NetworksLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Bagging PredictorsLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Introduction to Statistical Pattern RecognitionLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Self-Organizing MapsLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→The self-organizing mapLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Experiments with a new boosting algorithmLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Random decision forestsLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Instance-Based Learning AlgorithmsLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→The Handbook of brain theory and neural networksLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
Pattern Recognition and Neural Networks→Boosting a Weak Learning Algorithm by MajorityLegacy edge — source detail unavailable
Semi-Supervised Learning→Bayesian Network ClassifiersLegacy edge — source detail unavailable
Semi-Supervised Learning→Support-Vector NetworksLegacy edge — source detail unavailable
Semi-Supervised Learning→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
Semi-Supervised Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Semi-Supervised Learning→Bagging PredictorsLegacy edge — source detail unavailable
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→Neural networks for pattern recognitionLegacy edge — source detail unavailable
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Greedy function approximation: A gradient boosting machine.→Experiments with a new boosting algorithmLegacy edge — source detail unavailable
Greedy function approximation: A gradient boosting machine.→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Gaussian Processes for Machine Learning→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Gaussian Processes for Machine Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Support Vector Data Description→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Support Vector Data Description→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Logistic regression and artificial neural network classification models: a methodology re…→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Logistic regression and artificial neural network classification models: a methodology re…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
Logistic regression and artificial neural network classification models: a methodology re…→The Elements of Statistical Learning: Data Mining, Inference, and PredictionLegacy edge — source detail unavailable
Experiments with a new boosting algorithm→Boosting a Weak Learning Algorithm by MajorityLegacy edge — source detail unavailable
Experiments with a new boosting algorithm→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
Experiments with a new boosting algorithm→Bagging PredictorsLegacy edge — source detail unavailable
Fundamentals of Artificial Neural Networks→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Approximation theory of the MLP model in neural networks→The Handbook of brain theory and neural networksLegacy edge — source detail unavailable
Approximation theory of the MLP model in neural networks→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Ensemble Machine Learning→Neural networks for pattern recognitionLegacy edge — source detail unavailable
Ensemble Machine Learning→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
Ensemble Machine Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
The Nature of Statistical Learning Theory	1995	39,409
Support-Vector Networks	1995	33,914
Neural networks for pattern recognition	1994	18,720
Bagging Predictors	1996	17,191
Introduction to Statistical Pattern Recognition	1990	11,140
Self-Organizing Maps	1995	10,390
The self-organizing map	1990	8,234
Experiments with a new boosting algorithm	1996	7,582
Random decision forests	2002	5,123
Instance-Based Learning Algorithms	1991	4,128
The Handbook of brain theory and neural networks	1996	3,915
The Strength of Weak Learnability	1990	3,344
Neural Network Ensembles, Cross Validation, and Active Learning	1994	1,803
Boosting a Weak Learning Algorithm by Majority	1995	1,691

What cites it, inside the corpus

Paper	Year	Cited
Greedy function approximation: A gradient boosting machine.	2001	30,137
The Elements of Statistical Learning: Data Mining, Inference, and Prediction	2010	12,807
Gaussian Processes for Machine Learning	2005	10,470
Bayesian Network Classifiers	1997	4,754
Model-Based Clustering, Discriminant Analysis, and Density Estimation	2002	4,342
Semi-Supervised Learning	2006	4,334
Support Vector Data Description	2003	3,526
Introduction to Bayesian Networks	2019	2,594
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…	1998	2,510
Logistic regression and artificial neural network classification models: a methodology re…	2002	2,201
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation	2010	1,988
Fundamentals of Artificial Neural Networks	1996	1,902
Ensemble Machine Learning	2012	1,653
Approximation theory of the MLP model in neural networks	1999	1,548
Pattern Recognition and Machine Learning	2007	1,390
Feature subset selection using a genetic algorithm	1998	1,369

Links

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Topics

Neural Networks and Applications	Computer Science
Face and Expression Recognition	Computer Science
Spectroscopy and Chemometric Analyses	Chemistry

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complete

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supports1 author record(s) attached.
supports102 reference(s) recorded.
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supportsA title is present.

Provenance

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