Relevant bibliographies by topics / Machine Learning, Graphical Models, Kernel Methods, Optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Machine Learning, Graphical Models, Kernel Methods, Optimization'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Machine Learning, Graphical Models, Kernel Methods, Optimization"

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Deist, Timo M., Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, and David Craft. "Simulation-assisted machine learning." Bioinformatics 35, no. 20 (2019): 4072–80. http://dx.doi.org/10.1093/bioinformatics/btz199.

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Abstract Motivation In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approxima
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Özöğür Akyüz, Süreyya, Gürkan Üstünkar, and Gerhard Wilhelm Weber. "Adapted Infinite Kernel Learning by Multi-Local Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 04 (2016): 1651004. http://dx.doi.org/10.1142/s0218001416510046.

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The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, o
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Lu, Shengfu, Sa Liu, Mi Li, Xin Shi, and Richeng Li. "Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2668–74. http://dx.doi.org/10.1166/jmihi.2020.3198.

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The paper constructed a depression classification model based on emotionally related eye-movement data and kernel extreme learn machine (ELM). In order to improve the classification ability of the model, we use particle swarm optimization (PSO) to optimize the model parameters (regularization coefficient C and the parameter σ in the kernel function). At the same time, in order to avoid to be caught in the local optimum and improve PSO's searching ability, we use improved chaotic PSO optimization algorithm and Gauss mutation strategy to increase PSO's particle diversity. The classification resu
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SEEGER, MATTHIAS. "GAUSSIAN PROCESSES FOR MACHINE LEARNING." International Journal of Neural Systems 14, no. 02 (2004): 69–106. http://dx.doi.org/10.1142/s0129065704001899.

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Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigat
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Abdelhamid, Abdelaziz A., El-Sayed M. El El-Kenawy, Abdelhameed Ibrahim, and Marwa M. Eid. "Intelligent Wheat Types Classification Model Using New Voting Classifier." Journal of Intelligent Systems and Internet of Things 7, no. 1 (2022): 30–39. http://dx.doi.org/10.54216/jisiot.070103.

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When assessing the quality of the grain supply chain's quality, it is essential to identify and authenticate wheat types, as this is where the process begins with the examination of seeds. Manual inspection by eye is used for both grain identification and confirmation. High-speed, low-effort options became available thanks to automatic classification methods based on machine learning and computer vision. To this day, classifying at the varietal level is still challenging. Classification of wheat seeds was performed using machine learning techniques in this work. Wheat area, wheat perimeter, co
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Ramasamy, Lakshmana Kumar, Seifedine Kadry, and Sangsoon Lim. "Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 290–98. http://dx.doi.org/10.11591/eei.v10i1.2098.

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Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimiza
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Zhao, Xutao, Desheng Zhang, Renhui Zhang, and Bin Xu. "A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236, no. 8 (2021): 3938–49. http://dx.doi.org/10.1177/09544062211050542.

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Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonl
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Alarfaj, Fawaz Khaled, Naveed Ahmad Khan, Muhammad Sulaiman, and Abdullah M. Alomair. "Application of a Machine Learning Algorithm for Evaluation of Stiff Fractional Modeling of Polytropic Gas Spheres and Electric Circuits." Symmetry 14, no. 12 (2022): 2482. http://dx.doi.org/10.3390/sym14122482.

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Fractional polytropic gas sphere problems and electrical engineering models typically simulated with interconnected circuits have numerous applications in physical, astrophysical phenomena, and thermionic currents. Generally, most of these models are singular-nonlinear, symmetric, and include time delay, which has increased attention to them among researchers. In this work, we explored deep neural networks (DNNs) with an optimization algorithm to calculate the approximate solutions for nonlinear fractional differential equations (NFDEs). The target data-driven design of the DNN-LM algorithm wa
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Mei, Wenjuan, Zhen Liu, Yuanzhang Su, Li Du, and Jianguo Huang. "Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction." Entropy 21, no. 9 (2019): 912. http://dx.doi.org/10.3390/e21090912.

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In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed
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Correa-Jullian, Camila, Sergio Cofre-Martel, Gabriel San Martin, Enrique Lopez Droguett, Gustavo de Novaes Pires Leite, and Alexandre Costa. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection." Energies 15, no. 8 (2022): 2792. http://dx.doi.org/10.3390/en15082792.

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Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their potential for efficiently handling large quantities of operational data for PHM purposes. This paper addresses the application of quantum kernel classification models for fault detection in wind turbine systems (WTSs). The analyzed data correspond to low-
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Dissertations / Theses on the topic "Machine Learning, Graphical Models, Kernel Methods, Optimization"

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Zhang, Xinhua, and xinhua zhang cs@gmail com. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding." The Australian National University. ANU College of Engineering and Computer Sciences, 2010. http://thesis.anu.edu.au./public/adt-ANU20100729.072500.

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Over the past two decades graphical models have been widely used as powerful tools for compactly representing distributions. On the other hand, kernel methods have been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. Graphical models are a powerful underlying formalism in machine learning. Their graph theoretic properties provide both an intuitive modular interface to model the interacting factors, and a data structure facilitating efficient learning and inference. T
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Rowland, Mark. "Structure in machine learning : graphical models and Monte Carlo methods." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287479.

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This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong connections to other domains such as theoretical computer science. At the same time, there has often been a gap between the success of many algorithms in this area in practice, and what can be explained by theory; thus, an important research effort is to bridge this gap. Random embeddings for dimensionality re
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Zhang, Xinhua. "Graphical Models: Modeling, Optimization, and Hilbert Space Embedding." Phd thesis, 2010. http://hdl.handle.net/1885/49340.

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Over the past two decades graphical models have been widely used as a powerful tool for compactly representing distributions. On the other hand, kernel methods have also been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. The following four areas are our focus. 1. Conditional random fields for multi-agent reinforcement learning. Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditio
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Book chapters on the topic "Machine Learning, Graphical Models, Kernel Methods, Optimization"

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Dral, Pavlo O., Fuchun Ge, Bao Xin Xue, et al. "MLatom 2: An Integrative Platform for Atomistic Machine Learning." In Topics in Current Chemistry Collections. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07658-9_2.

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AbstractAtomistic machine learning (AML) simulations are used in chemistry at an everincreasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neuralnetwork- based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.
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Conference papers on the topic "Machine Learning, Graphical Models, Kernel Methods, Optimization"

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Adeeyo, Yisa Ademola, Anuola Ayodeji Osinaike, and Gamaliel Olawale Adun. "Estimation of Fluid Saturation Using Machine Learning Algorithms: A Case Study of Niger Delta Sandstone Reservoirs." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212696-ms.

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Abstract Water Saturation (Sw) is a critical input to reserves estimation and reservoir modeling workflows which ultimately informs effective reservoir management and decision-making. Without laboratory analysis on expensive core data, Sw is estimated using traditional correlations—commonly Archie's equation. However, using such a correlation in routine petrophysical analysis for estimating reservoir properties on a case-by-case basis is challenging and time-consuming. This study employs a data-driven approach to model Sw in Niger Delta sandstone reservoirs using readily available geophysical
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Wang, Liwei, Suraj Yerramilli, Akshay Iyer, Daniel Apley, Ping Zhu, and Wei Chen. "Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71570.

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Abstract Scientific and engineering problems often require an inexpensive surrogate model to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners in surrogate modeling, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for a growing number of data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inferen
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