An image classification scheme using Naïve Bayes Classifier is proposed in this paper. The proposed Naive Bayes Classifier-based image classifier can be considered as the maximum a posteriori decision rule. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time when compared to conventional supervised or unsupervised learning algorithms. Compreh...

In this paper, we employed Naïve Bayes, Augmented Naïve Bayes, Tree Augmented Naïve Bayes, Sons & Spouses, Markov Blanket, Augmented Markov Blanket, Semi Supervised and Bayesian network techniques to rank web services. The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes. Here, the attributes...

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the per...

هدف: کارآفرینی یکی از عوامل کلیدی در توسعه اقتصادی و شاخص اساسی جوامع روبه‌رشد است. آنچه که کارآفرین را به آغاز فعالیت ترغیب می‌کند، انگیزه فرایند تبدیل یک فرد عادی است می‌تواند فرصت‌هایی ایجاد کند حداکثر رساندن ثروت کمک کند. هدف این پژوهش شناسایی طبقه‌بندی انگیزه‌های کارآفرینان می­باشد.طراحی/ روش‌شناسی/ رویکرد: با رویکرد مرور نظام­مند استفاده ماتریس شش سلولی دو مؤلفه جهت (کشش یا فشار) منبع (اق...

The Naïve Bayes document classifier has been used in many document classification algorithms [1], but is only really useful on a small subset of documents due to it’s many shortcomings [2]. By augmenting the basic functionality of the simple Naïve Bayes classifier, the classification algorithm can be applied to a much wider range of documents. This paper investigates the advantages which can be...

Abstract The Threshold-based Naïve Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original classifier. Tb-NB extracts sentiment from Natural Language text corpus and allows user not only to predict how much sentence positive (negative) but also quantify with numeric value. It based on estimation single threshold value that concurs define decision rule classifies in...

in today world of internet, it is important to feedback the users based on what they demand. moreover, one of the important tasks in data mining is classification. today, there are several classification techniques in order to solve the classification problems like genetic algorithm, decision tree, bayesian and others. in this article, it is attempted to classify researchers to “expert” and “no...