The Mysterious Optimality of Naive Bayes: Estimation of the Probability in the System of "Classifiers"

Oleg Kupervasser, Alexsander Vardy

备注：: 9 pages,1 figure, all changes in the second version is made by Kupervasser only
发表信息：: Pattern Recognition and Image Analysis, 2014, Vol. 24, No. 1
DOI：: 10.1134/S1054661814010088

英文摘要：

Bayes Classifiers are widely used currently for recognition, identification and knowledge discovery. The fields of application are, for example, image processing, medicine, chemistry (QSAR). But by mysterious way the Naive Bayes Classifier usually gives a very nice and good presentation of a recognition. It can not be improved considerably by more complex models of Bayes Classifier. We demonstrate here a very nice and simple proof of the Naive Bayes Classifier optimality, that can explain this interesting fact.The derivation in the current paper is based on arXiv:cs/0202020v1

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks

Idiap Research Institute（伊迪亚普研究所）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；University of Rome La Sapienza（罗马第一大学）

Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentum in the community. This is because (1) such algorithms cannot use CNN activations as input features; (2) they cannot be used as final layer of CNN architectures for end-to-end training , and (3) they are generally not scalable and hence cannot handle big data. This paper proposes a framework that addresses all these issues, thus bringing back NBNNs on the map. We solve the first by extracting CNN activations from lo…

2015-11-17cs.CV

Automatic image annotation base on Naive Bayes and Decision Tree classifiers using MPEG-7

University of Raparin（拉帕林大学）

Recently it has become essential to search for and retrieve high-resolution and efficient images easily due to swift development of digital images, many present annotation algorithms facing a big challenge which is the variance for represent the image where high level represent image semantic and low level illustrate the features, this issue is known as semantic gab. This work has been used MPEG-7 standard to extract the features from the images, where the color feature was extracted by using Scalable Color Descriptor (SCD) and Color Layout Descriptor (CLD), whereas the texture feature was ext…

2021-01-27cs.CV

New approach using Bayesian Network to improve content based image classification systems

National Engineering School of Sousse（苏塞国立工程学院）；Preparatory Institute of Engineering of Monastir（莫纳斯提尔工程预科学院）

This paper proposes a new approach based on augmented naive Bayes for image classification. Initially, each image is cutting in a whole of blocks. For each block, we compute a vector of descriptors. Then, we propose to carry out a classification of the vectors of descriptors to build a vector of labels for each image. Finally, we propose three variants of Bayesian Networks such as Naive Bayesian Network (NB), Tree Augmented Naive Bayes (TAN) and Forest Augmented Naive Bayes (FAN) to classify the image using the vector of labels. The results showed a marked improvement over the FAN, NB and TAN.

2012-04-07cs.CVcs.IR

Multiple models of Bayesian networks applied to offline recognition of Arabic handwritten city names

Research Unit SAGE, National Engineering School of Sousse（苏塞国家工程学院SAGE研究单位）

In this paper we address the problem of offline Arabic handwriting word recognition. Off-line recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwritten words recognition using multiple Bayesian networks. First, we cut the image in several blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level featu…

2013-01-18cs.CV

Towards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation

Sapienza University（罗马大学）；Idiap Research Institute（Idiap研究所）

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed …

2015-03-26cs.CV

Local Naive Bayes Nearest Neighbor for Image Classification

University of British Columbia（不列颠哥伦比亚大学）

We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor's local neighborhood. W…

2011-12-01cs.CV