Factor modeling for clustering high-dimensional time series by Zhang, Bo, Pan, Guangming, Yao, Qiwei, Zhou, Wang

“…We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. …”
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Value investing with machine learning: the South American market by Chen, Ye

“…The primary machine learning method employed in this study is LSTM (Long Short-Term Memory), which performs well with time series data such as financial data of companies. …”
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An artificial sensory neuron with tactile perceptual learning by Wan, Changjin, Chen, Geng, Fu, Yangming, Wang, Ming, Matsuhisa, Naoji, Pan, Shaowu, Pan, Liang, Yang, Hui, Wan, Qing, Zhu, Liqiang, Chen, Xiaodong

“…Furthermore, the recognition error rate can be dramatically decreased from 44% to 0.4% by integrating with the machine learning method. This work represents a step toward the design and use of neuromorphic electronic skin with artificial intelligence for robotics and prosthetics.…”
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Doing time : inducing temporal graphs by Bramsen, Philip James

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The study of Rashomon effects on machine learning : a case study on breast cancer by Wee, Yu Hui

“…This theory has been translated into a popular machine learning method: Random Forests which uses bootstrapping (bagging) algorithms to create a set of uncorrelated decision trees that together make the decision (prediction) of the final result. …”
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Health monitoring of induction motor with impedance analysis and artificial intelligence by Lee, Kai Quan

“…The proposed technique for detecting stator winding faults in induction motors discussed in this study is a non-intrusive Machine Learning method. The early stages of any stator winding faults can be detected by using frequency and impedance magnitude data as my main parameters. …”
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Learning Bregman distance functions for semi-supervised clustering by Wu, Lei., Hoi, Steven C. H., Jin, Rong., Zhu, Jianke., Yu, Nenghai.

“…We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. …”
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Layer-wise learning framework for deep networks by Yu, Haoyao

“…The case examples demonstrate a rational compromise regarding layer-wise trainability and precision while validating the applicability of the proposed layer-wise learning method to determine the optimal number of layers for real-world scenarios.…”
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Design of interactive multimedia courseware in data structures & algorithms - IV by Melly

“…Online learning has also become a more popular platform than the traditional classroom learning method. Data Structures and Algorithms as one of the key foundations that one should possess before developing software applications or getting in programming related field, is incorporated in this project as an E-Learning courseware. …”
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Generalized RBF feature maps for efficient detection by Vempati, S, Vedaldi, A, Zisserman, A, Jawahar, CV

“…Furthermore, we investigate a learning method using l1 regularization to encourage sparsity in the final vector representation, and thus reduce its dimension. …”

A generalized stereotypical trust model by Fang, Hui, Zhang, Jie, Sensoy, Murat, Thalmann, Nadia Magnenat

“…We then propose a fuzzy semantic decision tree (FSDT) learning method to construct trust stereotypes that generalizes over seller non-nominal attributes by splitting their values in a fuzzy manner, and generalizes over nominal attributes by replacing their specific values with more general terms according to the ontology. …”
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Joint Feature Learning for Face Recognition by Lu, Jiwen, Liong, Venice Erin, Wang, Gang, Moulin, Pierre

“…Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for face representation, we propose an unsupervised feature learning method to learn hierarchical feature representation. …”
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Data-driven approach for task-driven medical image reconstruction and analysis by Li, Changhao

“…Thus, it is necessary to figure out how the different deep learning method work and also important to apply them on different dataset. …”
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Incremental learning technologies for semantic segmentation by Yang, Yizhuo

“…To solve this problem, an incremental learning method: Combination of Old Prediction and Modified Label (COPML) is developed in this dissertation project. …”
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Minimum number of inertial measurement units needed to identify signiicant variations in walk patterns of overweight individuals walking on irregular surfaces by Sikandar, Tasriva, Rabbi, Mohammad Fazle, Kamarul Hawari, Ghazali, Altwijri, Omar, Almijalli, Mohammed, Ahamed, Nizam Uddin

“…We then used deep learning method to verify whether the IMU data recorded from the identified body locations could classify walk patterns across the surfaces. …”
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Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes by Bosschieter, Tomas M., Xu, Zifei, Lan, Hui, Lengerich, Benjamin J., Nori, Harsha, Painter, Ian, Souter, Vivienne, Caruana, Rich

“…We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. …”
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Image classification by multimodal subspace learning by Yu, Jun, Lin, Feng, Seah, Hock Soon, Li, Cuihua, Lin, Ziyu

“…According to the “Patch Alignment” Framework, we developed a new subspace learning method, termed Semi-Supervised Multimodal Subspace Learning (SS-MMSL), in which we can encode different features from different modalities to build a meaningful subspace. …”
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Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks by Chakraborty, Debasrita, Goswami, Debayan, Ghosh, Susmita, Ghosh, Ashish, Chan, Jonathan H., Wang, Lipo

“…Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. …”
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User Profiling Based on Nonlinguistic Audio Data by Shen, Jiaxing, Cao, Jiannong, Lederman, Oren, Tang, Shaojie, Pentland, Alex

“…Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. …”
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E-learning for mobile learning platform by MacInnes, Catriona

“…By studying the way people learn, methods can be created to increase learning potential and efficiency. …”
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