SMA N 1 Plumbon di peruntukkan untuk membantu dan memermudah siswa dalam belajar sehingga tidak kalah dengan kota-kota besar justru menjadikan malas meningkatkan ragam dari kenakalan siswa. bagaimana memodelkan klasifikasi beberapa algoritma studi kasus ini menerapkan naïve bayes menganalisa hak akses internet siswa, penerapan metode tersebut dapat dilihat akurasi kemudian pemakaian berdasarkan...

Naïve-Bayes classifiers (NB) support incremental learning. However, the lack of effective incremental discretization methods has been hindering NB’s incremental learning in face of quantitative data. This problem is further compounded by the fact that quantitative data are everywhere, from temperature readings to share prices. In this paper, we present a novel incremental discretization method ...

Semi-supervised learning involves constructing predictive models with both labelled and unlabelled training data. The need for semi-supervised learning is driven by the fact that unlabelled data are often easy and cheap to obtain, whereas labelling data requires costly and time consuming human intervention and expertise. Semi-supervised methods commonly use self training, which involves using t...

Classification is a classic data mining technique based on machine learning. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. Naïve Bayes is a commonly used classification supervised learning method to predict class probability of belonging. This paper proposes a new method of Naïve Bayes Algorithm in which we tried to find effective...

Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for compa...

In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifiers: Naïve-Bayes, tree augmented Naïve-Bayes (TANs), BN augmented NaïveBayes (BANs) and general BNs (GBNs), where the GBNs and BANs are learned using two variants of a conditional independence based BN-learning algorithm. Experimental results show the GBNs and BANs learned using the proposing learn...

Classification problems have dominated research on boosting to date. The application of boosting to regression problems, on the other hand, has received little investigation. In this paper we develop a new boosting method for regression problems. We cast the regression problem as a classification problem and apply an interpretable form of the boosted naïve Bayes classifier. This induces a regre...

Biometric verification of subjects as users of computers or other devices has mainly based on fingerprints, face, iris or other images. We developed biometric verification using eye movements to be measured with eye movement videocameras. We measured saccades using the same stimulation for each subject. Our data included signals recorded in two manners: electrooculographically from 30 subjects ...

This paper introduces our work in the TREC2005 SPAM track. Naïve Bayes and Littlestone’s Winnow are chosen as our basic classifiers. In our investigation, we found that when the structures of Ham and Spam are very different, the feature distributions of them vary a lot. Thus the factor of structure is introduced into our filter. Besides textual word feature, some kind of other features are also...