Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 39 for “"Bayes' Classifier"”.
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Improving the Prediction Accuracy of Text Data and Attribute Data Mining with Data Preprocessing
… have many existing classifying approaches, Naïve Bayes Classifier is good at classification because of its simplicity and effectiveness. The aim of this paper is to identify the impact of preprocessing the dataset on the performance of a Naïve Bayes Classifier. Naïve Bayes Classifier is suggested …
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Comparing Naïve Bayes Classifiers with Support Vector Machines for Predicting Protein Subcellular Location Using Text Features
… and apply a support vector machine (SVM) classifier to classify proteins into their respective locations. Both EpiLoc and HomoLoc’s prediction accuracy is comparable to that of state-of-the-art protein location prediction systems. However, in addition to accuracy, other factors such as …
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Clustered Naive Bayes
… In this thesis, I present the Clustered Naive Bayes classifier, a hierarchical extension of the classic Naive Bayes classifier that ties several distinct Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters. A priori, the model assumes that there exists a …
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A standards-based grading model to predict students' success in a first-year engineering course
… of variables used in each model. The Naive Bayes Classifier and an Ensemble model using a combination of models (i.e., Support Vector Machine, K-Nearest Neighbors, and Naive Bayes Classifier) had the best results among the seven tested models. This study identified possible threshold …
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Masquerade detection using fortified naive Bayes
… Roy Maxion and Kevin Killourhy utilized a Naive Bayes classifier for detection using enriched Unix command lines, which are command line entries that still contain flags and other data. They discovered a problem with users that they dubbed supermasqueraders. These were users that would avoid …
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Genealogy Extraction and Tree Generation from Free Form Text
… for the second subsystem, which trains a Naı̈ve Bayes classifier to predict relationships from free form text by examining the types of relationships for pairs of entities and their associated feature vectors. The last subsystem accumulates extracted relationships into family trees. When a …
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Binary tree classifier and context classifier
Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability …
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Difference-expansion based reversible data hiding and its steganalysis
… which used 12-dimensional feature vectors and a Bayes Classifier. The proposed steganalysis scheme steadily achieved a correct classification rate of 99%.
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Ontology-based annotation using naive Bayes and decision trees
… and model the annotations with a group of naive Bayes classifiers, then explore the inherent relationship among different components defined by the ontology using a probabilistic decision tree model. Our solution outperforms conventional text mining approaches by taking advantage of an ontology. …
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Aspects of generative and discriminative classifiers
… new terminology of generative and discriminative classifiers, research interest in classical statistical approaches to discriminant analysis has re-emerged in the machine learning community. In discriminant analysis, observations with features $\mathbf{x}$ measured are classified into classes …
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Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
… techniques were considered. The 2-Step Naive Bayes classifier was found to produce 100% subjectwise specificity and 94% subjectwise sensitivity (with 75% lesional specificity, 63% lesional sensitivity). Thus it correctly rejected 13/13 healthy subjects and colocalized lesions in 29/31 of the …
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Application of Machine Learning Techniques for Real-time Classification of Sensor Array Data
… Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A training dataset of 4 classes containing 136 …
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A Comparison of Case-Based Reasoning and Probabilistic Graphical Models in the Context of Learning from Observation
… for future improvement. We show that the Naive Bayes Classifier is better than a previously used PGM model in learning behavior in a vacuum cleaner domain and introduce a state-based retrieval technique in CBR and show that there is no once-size-fits-all approach to learn state-based behavior. …
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Prosthesis control using a nearest neighbor electromyographic pattern classifier
… a nearest neighbor electromyographic pattern classifier was investigated with both a real time microprocessor-based controller and offline computational facilities. Four active electrodes for myoelectric signal amplitude detection were interfaced with a microcomputer for data logging and …
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COMPOSE: Compacted object sample extraction a framework for semi-supervised learning in nonstationary environments
… the performance of COMPOSE against the optimal Bayes classifier, as well as the arbitrary subpopulation tracker algorithm, which addresses a similar environment referred to as extreme verification latency. Furthermore, using the real-world National Oceanic and Atmospheric Administration weather …
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A comparative analysis of machine learning algorithms for genome wide association studies
… Partitioning, Logistic Regression and Naive Bayes Classifier. The classification accuracy of these algorithms is calculated in terms of area under the receiver operating characteristic curve (AUC). Conclusively, the logistic regression model with binary classification seems to be the most …
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News and financial market
… prediction algorithms that are based on Naive Bayes Classifier and Adjusted Document Frequency-Inverse Document Frequency(ADFIDF) weighting. Although occurrence of features selected by ADFIDF weighting can usually represent volatility bursts in financial market, it has been unclear whether it …
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Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach
… Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies …
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Machine learning in astronomy
… to train, optimise and test a Minimum Error Classifier (MEC), a naive Bayes classifier, a k-Nearest Neighbours (kNN) algorithm, a Support Vector Machine (SVM) and the SkyNet artificial neural network.
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Machine Learning for Radio Frequency Interference Flagging
… specific machine learning algorithms; Naive Bayes Classifier, K-Nearest Neighbours Classifier, Random Forest Classifier, the U-Net convolution neural network and the Multilayer Perceptron. These algorithms are trained on real data, in which the ground truth includes inherent false positives, …
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