• Corpus ID: 57572968

Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation

  title={Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation},
  author={Wan-Ping Chen and Yuan-chin Ivan Chang},
The model interpretation is essential in many application scenarios and to build a classification model with a ease of model interpretation may provide useful information for further studies and improvement. It is common to encounter with a lengthy set of variables in modern data analysis, especially when data are collected in some automatic ways. This kinds of datasets may not collected with a specific analysis target and usually contains redundant features, which have no contribution to a the… 


Sparse linear discriminant analysis by thresholding for high dimensional data
In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the
Classification Using Kernel Density Estimates
A multiscale approach along with a graphical device leading to a more informative discriminant analysis than the usual approach based on a single optimum scale of smoothing for each class density estimate when there are more than two competing classes is presented.
Algorithms for maximum-likelihood bandwidth selection in kernel density estimators
The fixed-point algorithms proposed obtain the maximum likelihood bandwidth in few iterations, without performing an exhaustive bandwidth search, which is unfeasible in the multivariate case.
High Dimensional Classification Using Features Annealed Independence Rules.
The conditions under which all the important features can be selected by the two-sample t-statistic are established and the choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error.
An Unsupervised and Nonparametric Classification Procedure Based on Mixtures with Known Weights
Abstract I consider a new problem of classification into n(n ≥ 2) disjoint classes based on features of unclassified data. It is assumed that the data are grouped into m(M ≥ n) disjoint sets and
Minimax nonparametric classification - Part II: Model selection for adaptation
  • Yuhong Yang
  • Mathematics, Computer Science
    IEEE Trans. Inf. Theory
  • 1999
It is shown that with a suitable model selection criterion, the penalized maximum-likelihood estimator has a risk bounded by an index of resolvability expressing a good tradeoff among approximation error, estimation error, and model complexity.
Multiple-resolution classification with combination of density estimators
The tests show that the introduced algorithm is superior to the basic version based on one estimator per class, and the proposed practical implementation of such an algorithm with parameters of density estimators adjusted to minimise the misclassification probability is proposed.
Estimating Continuous Distributions in Bayesian Classifiers
This paper abandon the normality assumption and instead use statistical methods for nonparametric density estimation for kernel estimation, which suggests that kernel estimation is a useful tool for learning Bayesian models.
Penalized likelihood density estimation provides an effective approach to the nonparametric fitting of graphical models, with conditional independence struc- tures characterized via selective term
Consistency of Data-driven Histogram Methods for Density Estimation and Classification
We present general sufficient conditions for the almost sureL1-consistency of histogram density estimates based on data-dependent partitions. Analogous conditions guarantee the almost-sure risk