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Selection of generative models in classification

https://doi.org/10.1109/tpami.2006.82

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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B ayesian Model Selection

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Finite Mixture Models

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Discriminant Analysis and Statistical Pattern Recognition

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Bayesian Theory

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Wiley Series in Probability and Statistics

inferredcites →https://doi.org/10.1007/3-540-47979-1_8openalex-snapshot

Learning a Sparse Representation for Object Detection

inferredcites →https://doi.org/10.1007/978-3-540-24671-8_6openalex-snapshot

Weak Hypotheses and Boosting for Generic Object Detection and Recognition

inferredcites →https://doi.org/10.1007/s10994-005-0469-0openalex-snapshot

Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers

inferredcites →https://doi.org/10.1016/s0893-6080(03)00005-4openalex-snapshot

Singularities in mixture models and upper bounds of stochastic complexity

inferredcites →https://doi.org/10.1017/cbo9780511812651openalex-snapshot

Pattern Recognition and Neural Networks

inferredcites →https://doi.org/10.1023/a:1007465528199openalex-snapshot

Bayesian Network Classifiers

inferredcites →https://doi.org/10.1023/b:visi.0000029664.99615.94openalex-snapshot

Distinctive Image Features from Scale-Invariant Keypoints

inferredcites →https://doi.org/10.1080/01621459.1979.10481632openalex-snapshot

A Predictive Approach to Model Selection

inferredcites →https://doi.org/10.1080/01621459.1989.10478752openalex-snapshot

Regularized Discriminant Analysis

inferredcites →https://doi.org/10.1080/01621459.1995.10476572openalex-snapshot

Bayes Factors

inferredcites →https://doi.org/10.1080/01621459.1996.10476746openalex-snapshot

Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition

inferredcites →https://doi.org/10.1080/01621459.1997.10474044openalex-snapshot

Practical Bayesian Density Estimation Using Mixtures of Normals

inferredcites →https://doi.org/10.1109/34.865189openalex-snapshot

Assessing a mixture model for clustering with the integrated completed likelihood

inferredcites →https://doi.org/10.1109/72.329683openalex-snapshot

Network information criterion-determining the number of hidden units for an artificial neural network model

inferredcites →https://doi.org/10.1109/cvpr.2003.1211479openalex-snapshot

Object class recognition by unsupervised scale-invariant learning

inferredcites →https://doi.org/10.1109/cvpr.2005.174openalex-snapshot

Hierarchical Part-Based Visual Object Categorization

inferredcites →https://doi.org/10.1109/iccv.2003.1238407openalex-snapshot

Selection of scale-invariant parts for object class recognition

inferredcites →https://doi.org/10.1109/idc.1999.754220openalex-snapshot

Objective functions for maximum likelihood classifier design

inferredcites →https://doi.org/10.1109/tac.1974.1100705openalex-snapshot

A new look at the statistical model identification

inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02028.xopenalex-snapshot

Predictive Model Selection

inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02073.xopenalex-snapshot

Discriminant Analysis by Gaussian Mixtures

inferredcites →https://doi.org/10.1137/1026034openalex-snapshot

Mixture Densities, Maximum Likelihood and the EM Algorithm

inferredcites →https://doi.org/10.1145/1015330.1015339openalex-snapshot

Learning Bayesian network classifiers by maximizing conditional likelihood

inferredcites →https://doi.org/10.1198/016214502760047131openalex-snapshot

Model-Based Clustering, Discriminant Analysis, and Density Estimation

inferredcites →https://doi.org/10.1214/aos/1176344136openalex-snapshot

Estimating the Dimension of a Model

inferredcites →https://doi.org/10.1214/ss/1009212519openalex-snapshot

Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors

inferredcites →https://doi.org/10.2307/2290355openalex-snapshot

Discriminant Analysis and Statistical Pattern Recognition.

inferredcites →https://doi.org/10.2307/2291091openalex-snapshot

Bayes Factors

inferredcites →https://doi.org/10.2307/271063openalex-snapshot

Bayesian Model Selection in Social Research

inferredcites →https://doi.org/10.48550/arxiv.1301.2284openalex-snapshot

Classifier Learning with Supervised Marginal Likelihood

inferredcites →https://doi.org/10.5555/777092.777121openalex-snapshot

Structural extension to logistic regression: discriminative parameter learning of belief net classifiers

inferredcites →https://doi.org/10.7551/mitpress/4175.001.0001openalex-snapshot

Learning with Kernels

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Discriminant Analysis

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Tool Wear Monitoring with Hidden Markov Models

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Mixture Models for Classification

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Bayesian Inference

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Bayesian Inference

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Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm

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A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection

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An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation

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Semi-supervised soft sensor development based on dynamic dimensionality reduction-assisted large-scale pseudo label optimization and sample-weighted quality-relevant deep learning

inferred← citeshttps://doi.org/10.1016/j.csda.2012.12.008openalex-snapshot

Model-based clustering of high-dimensional data: A review

inferred← citeshttps://doi.org/10.1016/j.csda.2013.02.010openalex-snapshot

A predictive deviance criterion for selecting a generative model in semi-supervised classification

inferred← citeshttps://doi.org/10.1016/j.eswa.2010.02.108openalex-snapshot

Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1

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Learning topology of a labeled data set with the supervised generative Gaussian graph

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Statistical mixture model for documents skew angle estimation

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Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA