نتایج جستجو برای: boosting and bagging strategies
تعداد نتایج: 16865484 فیلتر نتایج به سال:
previous studies regarding teachers’ beliefs have revealed that teachers’ beliefs have influence on their classroom practices. the current study aimed to investigate the effect of teachers’ beliefs about teaching reading strategies on students’ motivation and success in reading comprehension in the context of english teaching as a foreign language in high schools of mazandaran, iran. data were ...
Techniques for constructing classiier committees including Boosting and Bagging have demonstrated great success, especially Boosting for decision tree learning. This type of technique generates several classiiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classiication. Boosting and Bagging create diierent classi...
در این پایان نامه از روشهای یادگیری ماشینی به منظور تحلیل و ارزیابی امنیت دینامیک سیستم قدرت استفاده شده است. نحوه تولید پایگاه داده مناسب برای ارزیابی امنیت دینامیک سیستم قدرت و چالشهای رو به روی آن مورد بررسی قرار گرفته است. از یادگیرنده های گروهی به منظور افزایش دقت پیش بینی استفاده شده است.
Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability intrusion detection systems remain questionable current research, owing to particularly imbalanced data issue. The purpose of this article is address a gap literature by illustrating benefits ensemble-based models identifying threats attacks cyber-physical power...
the present study was carried on to investigate the use of face-keeping strategies in reaction to complaints by male and female in two different cultures, persian and american english. to see if apology strategies are affected by cultural, contextual preferences and gender of interlocutors in two distinct languages of persian and american english. the hypotheses to be tested were 1) ifids, acce...
An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new...
the present research was an attempt to see how quranic lexical collocations were translated into english by two professional translators namely, abdullah yusuf(2005), and muhammad s. shakir(2012). the study attempted qualitatively to shed light on how translators dealt with quranic lexical collocations when transferring them to the target language based on the newmark(1988) model , and quantit...
An ensemble consists of a set of independently trained classi ers such as neural networks or decision trees whose predictions are combined when classifying novel instances Previous re search has shown that an ensemble as a whole is often more accurate than any of the single classi ers in the ensemble Bagging Breiman a and Boosting Freund Schapire are two relatively new but popular methods for p...
Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these techniques with a trainable statistical parser are described. The best resulting system provides roughly as large of a gain in F-measure as doubling the corpus size. Error analysis of the result of the boosting technique reveals some inconsistent annotations in the P...
0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.04.191 ⇑ Corresponding author at: School of Management, ogy, Hefei, Anhui 230009, PR China. Tel.: +852 9799 0 E-mail address: [email protected] (G. Wang). With the rapid growth and increased competition in credit industry, the corporate credit risk prediction is becoming more important for credit-granting institutions. ...
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