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Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning [article]

Frank E. Curtis, Katya Scheinberg
2017 arXiv   pre-print
The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning.  ...  We begin by deriving a formulation of a supervised learning problem and show how it leads to various optimization problems, depending on the context and underlying assumptions.  ...  optimization algorithm learning rate Motivating illustration The idea of machine learning arises with the fundamental question of whether machines (i.e., computers) can "think" like humans.  ... 
arXiv:1706.10207v1 fatcat:mezejqzn3bgozjhgpafyick3xy

The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning [article]

Suyun Liu, Luis Nunes Vicente
2021 arXiv   pre-print
One can apply it to any stochastic MOO problem arising from supervised machine learning, and we report results for logistic binary classification where multiple objectives correspond to distinct-sources  ...  Optimization of conflicting functions is of paramount importance in decision making, and real world applications frequently involve data that is uncertain or unknown, resulting in multi-objective optimization  ...  Other models arising in supervised machine learning, such as the deep learning, could be also framed into an MOO context.  ... 
arXiv:1907.04472v3 fatcat:njr6cvyxtbcjvm7f5qqffj6ggm

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Toby Hocking, Guillem Rigaill, Guillaume Bourque
2015 International Conference on Machine Learning  
We investigate unsupervised and supervised learning of penalties for the critical model selection problem.  ...  Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e.g.  ...  In fact, each unsupervised peak detection algorithm has several numeric parameters, each with defaults that may or may not be optimal for a particular data set.  ... 
dblp:conf/icml/HockingRB15 fatcat:us5yecfjffcbrlxwmx22f4cdti

Optimization and Machine Learning Training Algorithms for Fitting Numerical Physics Models [article]

Raghu Bollapragada, Matt Menickelly, Witold Nazarewicz, Jared O'Neal, Paul-Gerhard Reinhard, Stefan M. Wild
2020 arXiv   pre-print
We analyze hyperparameter tuning considerations and variability associated with stochastic optimization algorithms and illustrate considerations for tuning in different computational settings.  ...  Of particular interest is the performance of optimization-based training algorithms when dozens, rather than millions or more, of training data are available and when the expense of the model places limitations  ...  DE-AC02-06CH11357 and by the NUCLEI SciDAC-4 collaboration. This work was also supported by the U.S.  ... 
arXiv:2010.05668v1 fatcat:grodvhoyvbhklhcaxttao2jrvi

Ridge-adjusted Slack Variable Optimization for supervised classification

Yinan Yu, Konstantinos I. Diamantaras, Tomas McKelvey, S. Y. Kung
2013 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  
Kernel techniques for classification is especially challenging in terms of computation and memory requirement when data fall into more than two categories.  ...  The main features of this technique are summarized as follows: (1) Only a subset of data are pre-selected to construct the basis for kernel computation; (2) Simultaneous active training set selection for  ...  However, a random data selection might degrade the performance. A breif comparison between random selection and Alg.  ... 
doi:10.1109/mlsp.2013.6661982 dblp:conf/mlsp/YuDMK13 fatcat:vieshyed25cfnixnardixzd4ue

An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors

Keerthana Jaganathan, Hilal Tayara, Kil To Chong
2022 Pharmaceutics  
Then, eight different machine learning algorithms are utilized to construct respiratory toxicity prediction models.  ...  The support vector machine classifier outperforms all other optimized models in 10-fold cross-validation. Additionally, it outperforms the prior study by 2% in prediction accuracy and 4% in MCC.  ...  Wrapper methods were used to find the optimal subset of descriptors for the specified machine learning algorithm.  ... 
doi:10.3390/pharmaceutics14040832 pmid:35456666 pmcid:PMC9028223 fatcat:qrikida4jvhchbssatnukq24em

A supervised machine-learning method for optimizing the automatic transmission system of wind turbines

Habeeb A. H. R. Aladwani, Mohd Khairol Anuar Ariffin, Faizal Mustapha
2022 Engineering Solid Mechanics  
In this research, an unsupervised machine-learning algorithm is proposed to address the energy efficiency of the automatic transmission system in vertical axis wind turbines (VAWT), to increase its efficiency  ...  Consequently, it is concluded that automatic VAWTs, equipped with the machine-learning capability can readjust themselves with the wind speed more efficiently.  ...  Acknowledgments Authors would like to thanks anonymous reviewers and the editor for their efforts during the publication process.  ... 
doi:10.5267/j.esm.2021.11.001 fatcat:mgcd7yz32nahreatp2cob7c4m4

Combining Trajectory Optimization, Supervised Machine Learning, and Model Structure for Mitigating the Curse of Dimensionality in the Control of Bipedal Robots [article]

Xingye Da, Jessy Grizzle
2017 arXiv   pre-print
To address this shortcoming, Supervised Machine Learning is used to extract a low-dimensional state-variable realization of the open-loop trajectories.  ...  The design procedure is first developed for ordinary differential equations and illustrated on a simple model.  ...  Acknowledgements The optimization and gait generation for the 3D walking and  ... 
arXiv:1711.02223v1 fatcat:za63vkgejfhajost5mfdwxdfny

Fast and simple gradient-based optimization for semi-supervised support vector machines

Fabian Gieseke, Antti Airola, Tapio Pahikkala, Oliver Kramer
2014 Neurocomputing  
One of the main learning tasks in machine learning is the one of classifying data items. The basis for such a task is usually a training set consisting of labeled patterns.  ...  A prominent research direction in the field of machine learning are semi-supervised support vector machines.  ...  The authors would like to thank the anonymous reviewers for valuable comments and suggestions on an early version of this work.  ... 
doi:10.1016/j.neucom.2012.12.056 fatcat:j4lxkbgemrel7gfqwqnsjk2dpq

Evaluating the Performance Estimators via Machine Learning Supervised Learning Algorithms for Dataset Threshold

Warda Imtiaz, Humaraia Abdul Ghafoor, Rabeea Sehar, Tahira Mahboob, Memoona Khanum
2015 International Journal of Computer Applications  
approaches of machine learning for supervised approaches and user modeling that is basically required for the handling of the label-data.  ...  Represented theorems provide the theoretical base for algorithms. 2.2) Unsupervised and Supervised Machine Learning in User Modeling for Intelligent Learning Environments (S.Amershi and C.Conati) Framework  ...  The resulting learning algorithms have connections with random walks, spectral graph theory, and electric networks. In Machine Learning supervised learning is dominant methodology.  ... 
doi:10.5120/21132-4059 fatcat:aozpcysea5d2hoh5aazftp6xjq

Scikit-learn: Machine Learning in Python [article]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Andreas Müller, Joel Nothman, Gilles Louppe, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas (+4 others)
2018 arXiv   pre-print
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.  ...  This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency.  ...  Conclusion Scikit-learn exposes a wide variety of machine learning algorithms, both supervised and unsupervised, using a consistent, task-oriented interface, thus enabling easy comparison of methods for  ... 
arXiv:1201.0490v4 fatcat:rz47i2dguvbuvp2glinpp4fx24

Machine learning in social epidemiology: Learning from experience

Catherine Kreatsoulas, S.V. Subramanian
2018 SSM: Population Health  
Medicine and disciplines related to health have become the new frontier for machine learning and big data.  ...  Table 1 An overview of the strengths and limitations of the machine learning approaches outlined by Seligman et al. (2018) . • Selecting the best model is more challenging than optimizing its parameters  ... 
doi:10.1016/j.ssmph.2018.03.007 pmid:29854919 pmcid:PMC5976835 fatcat:am5jdp76xncqdpfq6s4gu5qlry

Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

Jiang Hua, Liangcai Zeng, Gongfa Li, Zhaojie Ju
2021 Sensors  
The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots.  ...  Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21041278 pmid:33670109 pmcid:PMC7916895 fatcat:ehzsevmddfg5zlyc2wms6yuhui

Machine Learning for Fluid Mechanics [article]

Steven Brunton and Bernd Noack and Petros Koumoutsakos
2019 arXiv   pre-print
Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization.  ...  We outline fundamental machine learning methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows.  ...  We are grateful for discussions with Nathan Kutz (  ... 
arXiv:1905.11075v2 fatcat:brszpilzezc3xmbttdcla7zome

An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises [article]

Farid Ghareh Mohammadi, M. Hadi Amini, Hamid R. Arabnia
2019 arXiv   pre-print
Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (  ...  In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations.  ...  Traditionally, machine learning is a machine learns only input data and predict new data which follow the rule of the equation P i × D −→ M , where P i stands for the specific supervised algorithm parameters  ... 
arXiv:1908.09788v1 fatcat:qujten7zzzbd7laazhymnfw2yi
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