UTB Library OPAC Search for 'su:"Machine learning."'

How machines think : a general introduction to artificial intelligence ; illustrated in prolog

By Ford, Nigel. Chichester John Wiley 1987 . Pages: 206 0471911399

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Intelligence : the eye, the brain and the computer

By Fischler, Martin A.. Reading, Massachusetts Addison-Wesley Publishing Company 1987 . Pages: 331 , Includes index 24 cm.. 0201120011

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Introduction to machine learning

By Kodratoff, Yves. London Pitman Publishing. 1988 . Pages: 298 0273087967

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Artificial intelligence

By Time-Life Books.. Amsterdam Time-Life Books 1986 . Pages: 128. 0705409155

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Machine learning of natural language

By Powers, David M. W.. London Springer-Verlag 1989 . Pages: 358 0387195572

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Machine learning : principles and techniques

By Forsyth, Richard.. London Chapman and Hall 1989 . Pages: 255 0412305704

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On being a machine, volume 1 : formal aspects of artificial intelligence

By Narayanan, A.. Chichester Ellis Horwood Ltd. 1988 . Pages: 200 0853129576

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Thinking machines : the evolution of artificial intelligence

By Pratt, Vernon. Oxford Basil Blackwell 1987 . Pages: 254 0631149538

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Genetic algorithms in search optimization and machine learning /

By Goldberg, David E.,. Reading, Mass. : Addison-Wesley Pub. Co., 1989 . xiii, 412 p. : , includes index 25 cm.. 0201157675

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Pattern recognition and machine learning /

By Bishop, Christopher M.. India: Springer, 2006 . xx, 738 pages. : , Includes index 25 cm.. 9788132209065

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Introduction to machine learning /

By Alpaydin, Ethem.. Cambridge, Mass. ; | London : MIT Press, 2010 . xl, 537 pages : , Includes index 24 cm.. 9788120341609

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Bayesian reasoning and machine learning /

By Barber, David, . Cambridge : Cambridge University Press, 2012 . xxiv, 697 p. : 26 cm. . 9781107439955

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Machine learning : a probabilistic perspective /

By Murphy, Kevin P.,. Cambridge, Massachusetts : | London : MIT Press, 2012 . xxv, 1071 pages : 24 cm.. 9780262018029

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Introduction to machine learning /

By Alpaydin, Ethem.,. London The MIT Press 2014 . xxii, 613 pages : 24 cm.. 9780262028189

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Machine learning for audio, image and video analysis : theory and applications /

By Camastra, Francesco,. London : Springer, 2008 . xvi, 494 p. : 25 cm.. 9781447167341

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Pattern recognition and machine learning /

By Bishop, Christopher M.. New York : Springer, 2006 . xx, 738 pages. : , Includes index 25 cm.. 9780387310732

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Data mining : practical machine learning tools and techniques /

By Witten, Ian H.. Burlington, Massachusetts : Elsevier Inc. ; 2011 . xxxiii, 629 pages : 24cm.. 9780123748560

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Data Mining : practical machine learning tools and techniques /

By Witten, Ian H.. Cambridge, Massachusetts : Morgan Kaufmann ; 2017 . xxxii, 621 pages : 24cm.. 9780128042915

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Machine learning : the art and science of algorithms that make sense of data /

By Flach, Peter A.. Cambridge ; | New York : Cambridge University Press, 2012 . xvii, 396 p. : 25 cm.. 9781107096394 (hbk.) | 1107096391 (hbk.) | 9781107422223 (pbk.) | 1107422221 (pbk.)

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Deep learning neural networks : design and case studies /

By Graupe, Daniel. Danvers, U.S.A. : World Scientific Publishing Co. Pte. Ltd. ; 2016 . xvi, 263 pages : 25 cm.. 9789813146440

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Learning from data : a short course /

By Abu-Mostafa, Yaser S.,. United State : AMLbook.com , 2012 . xii, 201 pages : , Additional resources available online. 26 cm. 9781600490064 (hbk)

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Classification with imbalanced data: Ensemble method using split balancing and instance hardness /

By Chongomweru Halimu. Bandar Seri Begawan : Universiti Teknologi Brunei, 2021 . 158 pages : , Submitted in fulfillment of the requirements for the degree of Doctor of Philosophy. | Abstract In classification tasks, class imbalance and noise are among the top data quality challenges faced by the machine learning community when dealing with real-world data. A class imbalance problem happens when at least one class (majority class) has relatively, more instances than the other classes (minority classes). On the other hand, even though various definitions of noise have been presented in machine learning and statistics literature, most of them agree that noise presents inaccuracies that obscure the relationship between features of an object and its class. The presence of class imbalance and noise in a training set, which is almost unavoidable in real-world domains such as healthcare, finance, etc. generally degrades the classification performance of learning algorithms. There is an increasing demand for solving the problem of class imbalance while paying attention to the drawbacks of the existing methods that address them, and the underlying complexities existing within datasets. This research is motivated by the growing trends in the use of ensemble techniques for imbalanced classification. This is because of their ability to improve classification performance by leveraging the classification power of multiple base learners trained on different bootstraps of training data as compared to single learner classification algorithms. However, ensemble methods are still prone to the negative effects of class imbalance and noise in the training sets. Even though some of them attempt to address the problem of class imbalance by altering the original class distribution to create a sort of balance in the datasets through over-sampling or under-sampling, this can lead to overfitting or discarding useful data respectively, and thus may still hamper performance. Despite a relatively improved performance registered by some existing methods that utilize sampling-based techniques, much more work needs to be done to avoid discarding potentially useful data as well as minimizing the likelihood of overfitting. In recognition of the aforementioned challenges, this thesis focuses on binary classification problems, by first investigating the crucial issue of establishing a reliable and suitable performance evaluation metric for evaluating learner's performance with class imbalance problems. To achieve this, the study utilizes the earlier proposed criteria for comparing metrics based on their degree of Consistency and degree of Discriminancy, and empirically compares the performance of the most commonly used metrics for evaluation with class imbalance problems, namely; Area under the Receiver Operating Characteristic Curve (AUC), Mathew Correlation Coefficient (MCC), and Accuracy. The findings from the investigations demonstrate that both AUC and MCC are statistically consistent with each other, however, AUC is more discriminating than MCC. Hence, suggesting that AUC is a more reliable and suitable measure in evaluating binary class imbalance problems than MCC. Secondly, this thesis proposes an ensemble method for binary classification in imbalanced datasets using split balancing technique and instance hardness estimation. The proposed method creates an arbitrary number of balanced splits (sBal) of data generated based on Instance Hardness (IH) as a weighting mechanism for creating balanced bags. IH is referred to as a measure that specifies the degree of complexity in classifying a given instance in a dataset. This implies that each instance in a respective dataset has a property that suggests its probability of being classified incorrectly regardless of the choice of the classifier. i.e. instance with an estimated hardness value higher than a predefined threshold to is categorized as Hard, and with a hardness value below the threshold te is categorized as Easy, or else as Normal. Each of the generated bags contains all the minority instances, and a mixture of majority instances with varying degrees of hardness (easy, normal, and hard). This eliminates the possibility of discarding potentially useful data and enables base learners to train on different balanced bags comprising varied characteristics of the training data which might minimize overfitting. Comprehensive experiments were carried out to assess the effectiveness of the proposed method in dealing with the problem of noise and class imbalance. Performance of the proposed method is evaluated on a total of 100 datasets that include 30 synthetic datasets with controlled levels of noise, 29 balanced and 41 imbalanced real-world multi-domain datasets, and compared its performance with both traditional ensemble methods (Bagging, Wagging Random Forest, and AdaBoost), and those specialized for class imbalanced problems (Balanced Bagging, Balanced Random forest, RUSBoost, and Easy Ensemble). The results reveal that the proposed method brings a substantial improvement in classification performance relevant to the compared methods. Furthermore, the Equalized Loss of Accuracy (ELA) was calculated to assess the robustness of the proposed methods under different levels of noise. The results indicate that the proposed method is more robust (not affected by noise as much) as compared to the specialized ensemble method designed for handling class imbalance problems i.e. Balanced Bagging. To ascertain and validate the findings; throughout this research, a detailed and comprehensive statistical significance analysis of all the findings is carried out. The study first reviews the current practices in the machine learning community and theoretically examines various statistical tests to establish and recommend a set of nonparametric statistical tests suitable for validating the current experiments i.e. comparison of two or more classifiers. The statistical analysis shows that the proposed method performs significantly better than the compared traditional and specialized existing ensemble methods for imbalanced problems across many datasets. 30 cm..

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Machine Learning Based Sleep Interval Deciding Algorithm In Time Division Multiplexing Passive Optical Network (TDM-PON) /

By Dk. Siti Nur Amalina Binti Pg. Hj. Damit. Brunei Darussalam : Universiti Teknologi Brunei , 2019 . xiv, 89 pages : , The project report is submitted in fulfillment of the requirements for Master by Coursework in Information Security. | Abstract The study of Passive Optical Network (PON) standard as a next generation broadband optical access network in providing large capacity of bandwidth and saving energy consumption capability compared to other access technologies (such as Point-to-point), has always been a favorite subject to look onto. Since Information Communication Technology (ICT) is widely arise, Time-Division Multiplexing Passive Optical Network (TDM-PON) appears to be a promising technology that can meet users demand requirement. However, to keep the offer of ability to cope with the demand in TDM-PON, making further effort towards necessary approach is obligatory, and based on research found in academia and industry as well; there are still possible ways to make an enhancement towards energy efficiency. Popular saving energy approach wherein already made by TDM-PON, is to tum Optical Network Unit (ONU) into sleep state when nothing to transmit or receive. However, the approach could arise risk in the case of letting ONU to switch into sleep state in a not at a right moment. Optical Line Transmission (OLT) will not transmit the data when ONU is inactive or sleep. Therefore, this can made OLT to suffer and unfortunately causing delay, which then deteriorate the Quality of System (QoS) that could contradict PON's benefit that PON originally bring in. Hence, the sleep intervals can give impact towards delay in traffic. Delay experienced by downlink's traffic and energy it consumes can cause TDM-PON with sleep-mode method to be born with a compromise issue due to tradeoff problem. Concerning matter mentioned above, our goal is then established which is to save energy while also meeting network operator's access delay requirement. There are several studies related to this and we realize that most of existing work's solution utilize average value in order to forecast traffic arrival. Whereas here, our approach is to use machine learning and real traffic traces. We determine a suitable machine learning of Auto Regression Integrated Moving Average (ARIMA) model. We do also have evaluated our solution in real traffic case based on prediction, delay and energy saving performance in this thesis. It is to the best of our knowledge that our contribution by taking the concept of machine learning and digital twin based traffic arrival behavior as the first proposed solution towards trying to tackle the issue arise in achieving appropriate feasible sleep time in TDM-PON sleep state approach within this research domain. | Thesis is also available in CD and is not for loan or reference use. 30 cm..

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Application of machine learning for asset revenue optimization through equipment performance prediction using inline instrument data : a surface condenser case study/

By Mohamad Firdaus bin Mohammed Basheer. Bandar Seri Begawan : Universiti Teknologi Brunei, 2024 . xii, 236 pages : , Submitted in PHD fulfilment of the requirements for the degree of Doctor of Philosophy. | Abstract Equipment performance assessment or prediction has usually been derived using the conventional approach and heavily relies on expert opinion, where the methods used often vary from one person to another depending on the knowledge they possess. Many assets in the manufacturing industries are fitted with sensors on their equipment and this sensor could deliver values from both technical and business aspects. In this research work, a surface condenser was used as a case study to investigate machine learning methods that could be applied to the inline instrument data to predict the performance of the surface condenser and subsequently develop a cost-effective model for maintenance intervention decision-making. To date, most studies only focus on the technical improvement and prediction of specific technical parameters of surface condensers, and none of the research explores the holistic approach in attempting to link technical prediction to operating cost savings. In this study, the excess consumption (steam, condensate and water) due to the surface condenser inefficiencies were predicted through the comparison of Deep Learning method, Multi-Layer Perceptron Regression method, Support Vector Regression method and Multivariate Time Series – LSTM method. The Multivariate Time Series -LSTM method responds well to one year training time span with MAPE of 25% compared to MAPE of 43% for four year training time span. The deep learning method was seen as the most promising method, with an average EVS score of 88% for the multi-output prediction of steam, condensate, and water compared to EVS score of 74% and 50% for NNMLPR and SVR respectively. Based on the outcome of the economics assessment in justifying the performance of the surface condenser predicted effectiveness from 0.37 to 0.68, about 4%, 7% and 17% cost savings can be realised from the removal of wastes from steam, condensate and cooling water which equates to a total of $3,014,513.36 based on the same production capacity and period of test data. 30 cm..

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