Search Results - fine-tuning naive Bayesian algorithm

Lazy fine-tuning algorithms for naïve Bayesian text classification by El Hindi, Khalil M., Aljulaidan, Reem R., AlSalman, Hussien

“… In this study, we propose a lazy fine-tuning naïve Bayes (LFTNB) method to address both problems…”
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A noise tolerant fine tuning algorithm for the Naïve Bayesian learning algorithm by Khalil El Hindi

“…This work improves on the FTNB algorithm to make it more tolerant to noise. The FTNB algorithm augments the Naïve Bayesian (NB…”
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Building an Ensemble of Fine-Tuned Naive Bayesian Classifiers for Text Classification by El Hindi, Khalil, AlSalman, Hussien, Qasem, Safwan, Al Ahmadi, Saad

“…Text classification is one domain in which the naive Bayesian (NB) learning algorithm performs remarkably well…”
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Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification by Diab, Diab M., El Hindi, Khalil M.

“…•Initial population is generated by a method used for fine-tuning the NB, namely, FTNB.•DE algorithm using a multi-parent mutation and crossover operations (MPDE) is proposed…”
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Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model by He, Weiguo, Yin, Deyang, Zhang, Kaifeng, Zhang, Xiangwen, Zheng, Jianyong

“… To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal…”
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Fine tuning the tree augmented Naïve Bayes (FTTAN) learning algorithm by Alhussan, Amel, El Hindi, Khalil

“…In this work, we adapt the fine tuning algorithm of Naïve Bayes (NB) for Tree Augmented Naïve Bayes (TAN…”
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Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios by Avila, Eduardo, Kahmann, Alessandro, Alho, Clarice, Dorn, Marcio

“… Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms…”
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Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images by Bechelli, Solene, Delhommelle, Jerome

“…) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL…”
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Multi-label classification of symptom terms from free-text bilingual adverse drug reaction reports using natural language processing by Chaichulee, Sitthichok, Promchai, Chissanupong, Kaewkomon, Tanyamai, Kongkamol, Chanon, Ingviya, Thammasin, Sangsupawanich, Pasuree

“… Recently, the effectiveness of machine algorithms in natural language processing (NLP) has been widely…”
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Probability knowledge acquisition from unlabeled instance based on dual learning by Zhao, Yuetan, Wang, Limin, Zhu, Xinyu, Jin, Taosheng, Sun, Minghui, Li, Xiongfei

“…The functionality of machine learning algorithms heavily relies on the abundance and quality of training data accessible…”
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Deep Embedding Sentiment Analysis on Product Reviews Using Naive Bayesian Classifier by Sahithya, Nukabathini Mary Saroj, Prathyusha, Manda, Rachana, Nakkala, Priyanka, Perikala, Jyothi, P. J.

“… Deep learning is a class of machine learning algorithms that learn in supervised and unsupervised manners…”
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Customization of a DADA2-based pipeline for fungal internal transcribed spacer 1 (ITS1) amplicon data sets by Rolling, Thierry, Zhai, Bing, Frame, John, Hohl, Tobias M., Taur, Ying

“…Identification and analysis of fungal communities commonly rely on internal transcribed spacer-based (ITS-based) amplicon sequencing. There is no gold standard…”
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Customization of a dada2-based pipeline for fungal Internal Transcribed Spacer 1 (ITS 1) amplicon datasets by Rolling, Thierry, Zhai, Bing, Frame, John V, Hohl, Tobias, Taur, Ying

“… the accuracy of sample inference. By fine-tuning quality filtering, we decreased the number of wrongly discarded sequences attributed to Aspergillus species, Saccharomyces…”
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Detecting Textual Propaganda Using Machine Learning Techniques by Khanday, Akib Mohi Ud Din, Khan, Qamar Rayees, Rabani, Syed Tanzeel

“… In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms…”
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Applying Deep Belief Networks to Word Sense Disambiguation by Wiriyathammabhum, Peratham, Kijsirikul, Boonserm, Takamura, Hiroya, Okumura, Manabu

“… Then, a separate fine tuning step is employed to improve the discriminative power. We compared DBN with various state-of-the-art supervised learning algorithms in WSD such as Support Vector Machine (SVM…”
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Large-Scale Text Classification with Deep Neural Networks by Jo, Hwiyeol, Kim, Jin-Hwa, Kim, Kyung-Min, Chang, Jeong-Ho, Eom, Jae-Hong, Zhang, Byoung-Tak

“… Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows…”
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깊은 신경망 기반 대용량 텍스트 데이터 분류 기술 by 조휘열, 김진화, 김경민, 장정호, 엄재홍, 장병탁, Jo, Hwiyeol, Kim, Jin-Hwa, Kim, Kyung-Min, Chang, Jeong-Ho, Eom, Jae-Hong, Zhang, Byoung-Tak

“… Naïve Bayesian Classifier < Support Vector Machine (SVM) < LSTM < CNN < GRU, in order. The result can be interpreted as follows…”
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