Designing Model Based Classifiers by Emphasizing Soft Targets

No abstract is available for this paper.

• Publication year

  2009

• Venue

  Fundamenta Informaticae

• Publication date

  2009-11-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3233/FI-2009-186
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Probabilistic Inference Using Markov Chain Monte Carlo Methods

  2011cited by this paper
• Gaussian Processes for Machine Learning (GPML) Toolbox

  2010cited by this paper
• An emphasized target smoothing procedure to improve MLP classifiers performance

  2008cited by this paper
• Mixtures of robust probabilistic principal component analyzers

  2008cited by this paper
• A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles

  2008cited by this paper
• Applying emphasized soft targets for Gaussian Mixture Model based classification

  2008cited by this paper
• Robust Bayesian clustering

  2007cited by this paper
• Boosting by weighting critical and erroneous samples

  2006cited by this paper
• Pattern Recognition and Machine Learning

  2006cited by this paper
• Bayesian Gaussian Process Classification with the EM-EP Algorithm

  2006cited by this paper
• Pattern Recognition and Machine Learning (Information Science and Statistics)

  2006cited by this paper
• Sparse Gaussian Processes using Pseudo-inputs

  2005cited by this paper
• Assessing Approximate Inference for Binary Gaussian Process Classification

  2005cited by this paper
• The training of neural classifiers with condensed datasets

  2002cited by this paper
• Nonlinear kernel-based statistical pattern analysis

  2001cited by this paper
• Expectation Propagation for approximate Bayesian inference

  2001cited by this paper
• A family of algorithms for approximate Bayesian inference

  2001cited by this paper
• An introduction to kernel-based learning algorithms

  2001cited by this paper
• Generalization and Selection of Examples in Feedforward Neural Networks

  2000cited by this paper
• Moderating the outputs of support vector machine classifiers

  1999cited by this paper
• Sample selection via clustering to construct support vector-like classifiers

  1999cited by this paper
• Markov Chain Monte Carlo in Practice: A Roundtable Discussion

  1998cited by this paper
• Improved Boosting Algorithms Using Confidence-rated Predictions

  1998cited by this paper
• UCI Repository of machine learning databases

  1998cited by this paper
• Bayesian Classification With Gaussian Processes

  1998cited by this paper
• The New ERA in Supervised Learning

  1997cited by this paper
• Experiments with a New Boosting Algorithm

  1996cited by this paper
• Game theory, on-line prediction and boosting

  1996cited by this paper
• Pattern Recognition and Neural Networks

  1995cited by this paper
• Support-Vector Networks

  1995cited by this paper
• Similarities of error regularization, sigmoid gain scaling, target smoothing, and training with jitter

  1995cited by this paper
• Decision-based neural networks with signal/image classification applications

  1995cited by this paper
• Neural Networks and Related Methods for Classification

  1994cited by this paper
• Pedagogical pattern selection strategies

  1994cited by this paper
• An Alternative Model for Mixtures of Experts

  1994cited by this paper
• Energy functions for minimizing misclassification error with minimum-complexity networks

  1994cited by this paper
• Hierarchical Mixtures of Experts and the EM Algorithm

  1993cited by this paper
• Selecting concise training sets from clean data

  1993cited by this paper
• A training algorithm for optimal margin classifiers

  1992cited by this paper
• Repeat Until Bored: A Pattern Selection Strategy

  1991cited by this paper
• A Competitive Modular Connectionist Architecture

  1990cited by this paper
• Locally Trained Piecewise Linear Classifiers

  1980cited by this paper
• The condensed nearest neighbor rule (Corresp.)

  1968cited by this paper
• The Condensed Nearest Neighbor Rule

  1967cited by this paper
• The perceptron: a probabilistic model for information storage and organization in the brain.

  1958cited by this paper
• THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

  1936cited by this paper

• Smart Watchdog: A Lightweight Defending Mechanism Against Adversarial Transfer Learning

  2020cites this paper
• On building ensembles of stacked denoising auto-encoding classifiers and their further improvement

  2018cites this paper
• A new boosting design of Support Vector Machine classifiers

  2015cites this paper
• B lancos Blandos Enfatizados para Clasificacion Maquina

  2011cites this paper
• A pseudoregression formulation of emphasized soft target procedures for classification problems

  2010cites this paper