We consider the problem of image classification when more than one visual feature is available. In such cases, Bayes fusion offers an attractive solution by combining the results of different classifiers (one classifier per feature). This is the general form of the so-called ‘‘naive Bayes’’ approach. This paper compares the performance of Bayes fusion with respect to Bayesian classification, wh...

Naive Bayes is a fast to train model for sequence classification. We develop and experiment with two methods that are equally fast (they require only one pass through the training data), but they are also able to models interactions among close neighbours in the sequence (unlike the Naive bayes independence assumption). The first method basically runs Naive Bayes on overlapping k-grams obtained...

Bayes factors have been ooered by Bayesians as alternatives to P-values (or significance probabilities) for testing hypotheses and for quantifying the degree to which observed data support or connict with a hypothesis. Schervish (1996) showed how the interpretation of P-values as measures of support suuers a certain logical aw. In this paper, we show how Bayes factors suuer that same aw. We inv...

Prediksi adalah upaya memperkirakan sesuatu yang akan terjadi di masa depan dengan menggunakan berbagai informasi relevan pada waktu sebelumnya metode ilmiah. Data mining kumpulan data atau memiliki fungsi dan sangat bermanfaat mendatang. Pemanfaatan juga tidak hanya untuk teknologi saja melainkan bisa Bidang Kesehatan salah satunya memprediksi penyakit diabetes Naive Bayes yaitu algoritma memi...

We propose two general heuristics to transform a batch Hill-climbing search into an incremental one. Our heuristics, when new data are available, study the search path to determine whether it is worth revising the current structure and if it is, they state which part of the structure must be revised. Then, we apply our heuristics to two Bayesian network structure learning algorithms in order to...

2 Getting started 2 2.1 What is clustering? . . . . . . . . . . . . . . . . . . . 2 2.2 Target data . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Naive Bayes models . . . . . . . . . . . . . . . . . . . 3 2.3.1 Overall structure . . . . . . . . . . . . . . . . 3 2.3.2 Attribute distribution (in general) . . . . . . . 3 2.3.3 Attribute distribution (in detail) . . . . . . . . 3 2.4 Runni...

We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task. The extended model incorporates the idea the words closer to the target word are more relevant in predicting its sense. The proposed model is very simple yet effective when evaluated on SemEval-2010 WSI data.

The aphorism “All models are wrong but some are useful” (Box, 1978) sums up much of what ML is about. The assumptions we make in the Naive Bayes approach to sentimanet classification are wrong, but this is true of the assumptions made in all current formal models of human language (statistical or otherwise), with the possible exception of a few which are very restricted indeed. However, the que...