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Baytas, Inci Meliha, author.
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Contributions to Machine Learning in Biomedical Informatics / Baytas, Inci Meliha, author.
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Patel, Parth, author.
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Supervised Machine Learning for Small RNA Informatics and Big Data Analytics in Plants / Patel
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Bhattacharya, Moumita, author.
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Applying Machine Learning Methods to Electronic Health Records: Studies in Risk-Stratification of
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Dutta, Preetam K., author.
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Machine Learning Based User Modeling for Enterprise Security and Privacy Risk Mitigation / Dutta
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Shi, Ningxin, author.
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Semi-Supervised Machine Learning for Network Intrusion Detection / Shi, Ningxin, author.
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Choi, Chiyoung, author.
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Predicting Customer Complaints in Mobile Telecom Industry Using Machine Learning Algorithms / Choi
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Almada, Demetrius J., author.
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A Medley of Ising Models: Monte Carlo Solutions and Machine Learning Applications / Almada
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Fakhry, Carl Tony, author.
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Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms / Fakhry, Carl Tony
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Chimmiri, Baby Sri Pravallika, author.
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An Ontological Approach and a Meta-Algorithm for Data and Machine Learning Algorithms Analysis and
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Franklin, Bryan M., author.
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Automatic Selection of MapReduce Machine Learning Algorithms: A Model Building Approach / Franklin
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Islam, S. M. Ashiqul, author.
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Machine Learning-assisted Prediction of Structure and Function of Cystine-stabilized Peptides and
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Yuan, Yang, author.
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Provable and Practical Algorithms for Non-Convex Problems in Machine Learning / Yuan, Yang, author.
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Ghosh, Eshaan, author.
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Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application /
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Cai, Ermao, author.
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Power/Performance Modeling and Optimization: Using and Characterizing Machine Learning Applications /
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Angola Abreu, Enrique D., author.
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Novelty Detection of Machinery Using a Non-parametric Machine Learning Approach / Angola Abreu
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Islam, S. M. Ashiqul, author.
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Machine Learning-Assisted Prediction of Structure and Function of Cystine-Stabilized peptides and
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Guan, Meijian, author.
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Incorporating EMR and Genomic Data Using NLP and Machine Learning to Refine Cancer Treatment / Guan
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da Rosa, Raquel C., author. (orcid)0000-0003-2055-3887
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An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S
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Banerjee, Debrup, author. (orcid)0000-0003-3270-3600
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Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection / Banerjee
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Bayat, Akram, author.
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From Motor Control to Scene Perception: Using Machine Learning to Model Human Behavior and
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Young, Christina B., author.
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Connectivity, and Machine Learning Approaches / Young, Christina B., author.
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Das, Sourav, author. (orcid)0000-0002-7404-8133
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Towards Energy Efficient and Reliable 3D Manycore Chip Enabled by Machine Learning / Das, Sourav
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Gordon, David, author.
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Big Data Science: Applying Unsupervised and Supervised Machine Learning Algorithms to Predict and
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Fang, Zhou, author.
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Application of Machine Learning: An Analysis of Asian Options Pricing Using Neural Network / Fang
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Konkayala, Bhargava Reddy, author.
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Development of Machine Learning Methodologies to Improve Outcomes in Chronic Inflammatory Diseases /
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Zermas, Dimitris, author.
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Combining Machine Learning with Computer Vision for Precision Agriculture Applications / Zermas
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Nathan, Alexandros, author.
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Essays in Machine Learning, Social Networks and Marketing / Nathan, Alexandros, author.
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Wilber, Michael James, author. (orcid)0000-0001-7040-0251
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How might we teach machine learning systems about what wine tastes like, or how to appreciate the
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Kim, Clara Heown, author.
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Hypothesis: A machine learning (ML) algorithm using natural language processing will predict
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Yates, Heath Landon, author.
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environments. These contributions include exploring new methodologies in applying supervised machine learning
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Einsidler, Dylan, author.
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data. Machine learning techniques have made great strides in tackling this feat, although not much
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Olson, Matthew, author.
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A random forest is a popular machine learning ensemble method that has proven successful in solving
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Hallaji, Ehsan, author.
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diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Machine learning
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Encapera, Angelo Michael, author.
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machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in
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Wu, Tao, author.
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Markov random walk models are powerful analytical tools for multiple areas in machine learning
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Ma, Min, author. (orcid)0000-0002-1816-1772
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significant improvements by using the source application information. (3) Marrying machine learning algorithms
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Palomino, Norma, author.
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, helped engineer specific features that guided a machine learning classifier in predicting negated tweets
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Izenov, Yesdaulet, author.
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prediction task without ever storing input samples are the key concepts of streaming machine learning models
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Zhang, Chiyuan, author.
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notion in machine learning theory. We will show how, in the regime of deep learning, the characterization
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Kokate, Apurva, author. (orcid)0000-0003-2353-4171
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-critical decisions such as perception for self-driving cars, the machine learning community has been extremely
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Tokmic, Farah, author.
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, and (b) comparing the prevalence of stigma in different populations. Machine learning classification
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Prusa, Joseph Daniel, author. (orcid)0000-0001-7731-9894
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Many current application domains of machine learning and artificial intelligence involve knowledge
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Villalon, Rachelle B., author.
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/real-time using computer vision, machine learning, and statistical modeling techniques. The software
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Amand, Joseph St., author.
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a more effective metric typically results in more accurate metric-based machine learning algorithms.
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Fan, Bo, author.
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, oceanography, and engineering. In geoscience methods, statistical signal processing, modeling, and machine
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Huang, Xiao Bing, author. (orcid)0000-0002-1198-9526
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Deep Learning (DL) has found great success in well-diversified areas such as machine vision, speech
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Gardner, Jacob Ross, author.
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-calibrated uncertainty estimates. Machine learning models make mistakes, and by offering users full probabilistic
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Antropova, Natalia, author.
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lesion's size, shape, texture, and enhancement patters. The recent advances in machine learning
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Bonk, Brian M., author.
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In the second part, we apply methods from machine learning to an ensemble of reactive and non
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Koçak, Mustafa Anil, author.
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theoretical guarantees and the effectiveness of our approach through experiments on standard machine learning
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Noraset, Thanapon, author. (orcid)0000-0002-7080-6523
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research, machine learning models can construct general vector representations of words, also known as word
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Nabulsi, Ala-Addin, author.
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and applied to 4 different machine learning models.
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Kutt, Brody, author.
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real-time tasks. Unlike traditional machine learning and deep learning encompassed by the act of
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Le, Tuc Viet, author.
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Conceptually, this thesis uses machine learning techniques to solve three separate urban problems
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Murmuria, Rahul, author. (orcid)0000-0001-6327-9609
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continuous behavioral modeling of users for active authentication leveraging novel machine learning
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Filimon, Don, author.
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influencing behavior is the interaction between humans and robots along with machine learning techniques
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Isaza, Yueng Santiago De La Hoz, author.
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fall. Sensor data are usually processed using computer vision, data mining, and machine learning
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