Classifying with confidence using Bayes rule and kernel density estimation
نویسندگان
چکیده
منابع مشابه
Confidence intervals for kernel density estimation
This article describes asciker and bsciker, two programs that enrich the possibility for density analysis using Stata. asciker and bsciker compute asymptotic and bootstrap confidence intervals for kernel density estimation, respectively, based on the theory of kernel density confidence intervals estimation developed in Hall (1992b) and Horowitz (2001). asciker and bsciker allow several options ...
متن کاملKernel Bayes' Rule
A nonparametric kernel-based method for realizing Bayes’ rule is proposed, based on kernel representations of probabilities in reproducing kernel Hilbert spaces. The prior and conditional probabilities are expressed as empirical kernel mean and covariance operators, respectively, and the kernel mean of the posterior distribution is computed in the form of a weighted sample. The kernel Bayes’ ru...
متن کاملKernel Bayes' rule: Bayesian inference with positive definite kernels
A kernel method for realizing Bayes’ rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS. The prior and conditional probabilities are expressed in terms of RKHS functions of an empirical sample: no explicit parametric model is needed for these quantities. The poste...
متن کاملUnsupervised Discretization Using Kernel Density Estimation
Discretization, defined as a set of cuts over domains of attributes, represents an important preprocessing task for numeric data analysis. Some Machine Learning algorithms require a discrete feature space but in real-world applications continuous attributes must be handled. To deal with this problem many supervised discretization methods have been proposed but little has been done to synthesize...
متن کاملInformation Theoretic Clustering using Kernel Density Estimation
In recent years, information-theoretic clustering algorithms have been proposed which assign data points to clusters so as to maximize the mutual information between cluster labels and data [1, 2]. Using mutual information for clustering has several attractive properties: it is flexible enough to fit complex patterns in the data, and allows for a principled approach to clustering without assumi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Chemometrics and Intelligent Laboratory Systems
سال: 2019
ISSN: 0169-7439
DOI: 10.1016/j.chemolab.2019.04.004