Bibliografías temáticas / Machine learning. Computational learning theory. Combinatorial optimization

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Literatura académica sobre el tema "Machine learning. Computational learning theory. Combinatorial optimization"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "Machine learning. Computational learning theory. Combinatorial optimization"

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Wang, Zhaohao. "A New Description of Transversal Matroids Through Rough Set Approach." Fundamenta Informaticae 179, no. 4 (2021): 399–416. http://dx.doi.org/10.3233/fi-2021-2030.

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Matroid theory is a useful tool for the combinatorial optimization issue in data mining, machine learning and knowledge discovery. Recently, combining matroid theory with rough sets is becoming interesting. In this paper, rough set approaches are used to investigate an important class of matroids, transversal matroids. We first extend the concept of upper approximation number functions in rough set theory and propose the notion of generalized upper approximation number functions on a set system. By means of the new notion, we give some necessary and sufficient conditions for a subset to be a p
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Ansarifar, Javad, and Lizhi Wang. "New algorithms for detecting multi-effect and multi-way epistatic interactions." Bioinformatics 35, no. 24 (2019): 5078–85. http://dx.doi.org/10.1093/bioinformatics/btz463.

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AbstractMotivationEpistasis, which is the phenomenon of genetic interactions, plays a central role in many scientific discoveries. However, due to the combinatorial nature of the problem, it is extremely challenging to decipher the exact combinations of genes that trigger the epistatic effects. Many existing methods only focus on two-way interactions. Some of the most effective methods used machine learning techniques, but many were designed for special case-and-control studies or suffer from overfitting. We propose three new algorithms for multi-effect and multi-way epistases detection, with
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Khachai, M. Yu. "Computational complexity of combinatorial optimization problems induced by collective procedures in machine learning." Proceedings of the Steklov Institute of Mathematics 272, S1 (2011): 46–54. http://dx.doi.org/10.1134/s0081543811020040.

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Kaur, Harpreet. "Analysis of Various Optimization Techniques in Machine Learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 472–82. http://dx.doi.org/10.17762/turcomat.v12i2.855.

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Optimization model the goal of solving a task in the paramount way providing the best results and it implies a best view point for solving query or we can also define optimization as a way of probing an another solution with the maximum cost efficient and utmost practicable performance under the required constraints, by increasing the preferred factor and reducing the unprefferable ones. This increase provides the utmost result or inference without any obstacles or complications. In this paper, various optimization techniques with respect to image processing are studied, where image processing
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Peng, Yun, Byron Choi, and Jianliang Xu. "Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art." Data Science and Engineering 6, no. 2 (2021): 119–41. http://dx.doi.org/10.1007/s41019-021-00155-3.

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AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors
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Wu, Shaohua, Yong Hu, Wei Wang, Xinyong Feng, and Wanneng Shu. "Application of Global Optimization Methods for Feature Selection and Machine Learning." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/241517.

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The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. The process reduces the number of features by removing irrelevant and redundant data. This paper proposed a novel immune clonal genetic algorithm based on immune clonal algorithm designed to solve the feature selection problem. The proposed algorithm has more exploration and exploitation abilities due to the clonal selection theory, and each antibody in the search space specifies a subset of the possible features. Experimental results show that the proposed algorithm simplifies the fe
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Ramanathan, Kiruthika, and Sheng Uei Guan. "Clustering and combinatorial optimization in recursive supervised learning." Journal of Combinatorial Optimization 13, no. 2 (2006): 137–52. http://dx.doi.org/10.1007/s10878-006-9017-5.

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Hart, Emma, and Kevin Sim. "On Constructing Ensembles for Combinatorial Optimisation." Evolutionary Computation 26, no. 1 (2018): 67–87. http://dx.doi.org/10.1162/evco_a_00203.

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Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics ca
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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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Cranmer, Kyle, Matthew Drnevich, Sebastian Macaluso, and Duccio Pappadopulo. "Reframing Jet Physics with New Computational Methods." EPJ Web of Conferences 251 (2021): 03059. http://dx.doi.org/10.1051/epjconf/202125103059.

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We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization. We also review some of the recent research in this direction that has been enabled with Ginkgo. We show how probabilistic programming can b
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Tesis sobre el tema "Machine learning. Computational learning theory. Combinatorial optimization"

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Saket, Rishi. "Intractability results for problems in computational learning and approximation." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29681.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Khot, Subhash; Committee Member: Tetali, Prasad; Committee Member: Thomas, Robin; Committee Member: Vempala, Santosh; Committee Member: Vigoda, Eric. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Narasimhan, Mukund. "Applications of submodular minimization in machine learning /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5983.

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Ponnuswami, Ashok Kumar. "Intractability Results for some Computational Problems." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24638.

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In this thesis, we show results for some well-studied problems from learning theory and combinatorial optimization. Learning Parities under the Uniform Distribution: We study the learnability of parities in the agnostic learning framework of Haussler and Kearns et al. We show that under the uniform distribution, agnostically learning parities reduces to learning parities with random classification noise, commonly referred to as the noisy parity problem. Together with the parity learning algorithm of Blum et al, this gives the first nontrivial algorithm for agnostic learning of parities. We u
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(11196552), Kevin Segundo Bello Medina. "STRUCTURED PREDICTION: STATISTICAL AND COMPUTATIONAL GUARANTEES IN LEARNING AND INFERENCE." Thesis, 2021.

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<div>Structured prediction consists of receiving a structured input and producing a combinatorial structure such as trees, clusters, networks, sequences, permutations, among others. From the computational viewpoint, structured prediction is in general considered <i>intractable</i> because of the size of the output space being exponential in the input size. For instance, in image segmentation tasks, the number of admissible segments is exponential in the number of pixels. A second factor is the combination of the input dimensionality along with the amount of data under availability. In structur
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Nabli, Adel. "The multilevel critical node problem : theoretical intractability and a curriculum learning approach." Thesis, 2020. http://hdl.handle.net/1866/24329.

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Évaluer la vulnérabilité des réseaux est un enjeu de plus en plus critique. Dans ce mémoire, nous nous penchons sur une approche étudiant la défense d’infrastructures stratégiques contre des attaques malveillantes au travers de problèmes d'optimisations multiniveaux. Plus particulièrement, nous analysons un jeu séquentiel en trois étapes appelé le « Multilevel Critical Node problem » (MCN). Ce jeu voit deux joueurs s'opposer sur un graphe: un attaquant et un défenseur. Le défenseur commence par empêcher préventivement que certains nœuds soient attaqués durant une phase de vaccination. Ensuite,
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(9165011), Salar Safarkhani. "GAME-THEORETIC MODELING OF MULTI-AGENT SYSTEMS: APPLICATIONS IN SYSTEMS ENGINEERING AND ACQUISITION PROCESSES." Thesis, 2020.

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<div><div><div><p>The process of acquiring the large-scale complex systems is usually characterized with cost and schedule overruns. To investigate the causes of this problem, we may view the acquisition of a complex system in several different time scales. At finer time scales, one may study different stages of the acquisition process from the intricate details of the entire systems engineering process to communication between design teams to how individual designers solve problems. At the largest time scale one may consider the acquisition process as series of actions which are, request for
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Libros sobre el tema "Machine learning. Computational learning theory. Combinatorial optimization"

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(Editor), Martin Pelikan, Kumara Sastry (Editor), and Erick Cantú-Paz (Editor), eds. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence). Springer, 2006.

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Sastry, Kumara, Martin Pelikan, and Erick Cantú-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2010.

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Bi, Xiaojun, Andrew Howes, Per Ola Kristensson, Antti Oulasvirta, and John Williamson. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0001.

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This chapter introduces the field of computational interaction, and explains its long tradition of research on human interaction with technology that applies to human factors engineering, cognitive modelling, artificial intelligence and machine learning, design optimization, formal methods, and control theory. It discusses how the book as a whole is part of an argument that, embedded in an iterative design process, computational interaction design has the potential to complement human strengths and provide a means to generate inspiring and elegant designs without refuting the part played by th
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The Expected Knowledge: What can we know about anything and everything? Sivashanmugam Palaniappan, 2012.

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Capítulos de libros sobre el tema "Machine learning. Computational learning theory. Combinatorial optimization"

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Ahmed, Furqan, Muhammad Zeeshan Asghar, and Ali Imran. "Combinatorial Optimization for Artificial Intelligence Enabled Mobile Network Automation." In Metaheuristics in Machine Learning: Theory and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70542-8_27.

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Lissner, Jonah. "Atomistic Mathematical Theory for Metaheuristic Structures of Global Optimization Algorithms in Evolutionary Machine Learning for Power Systems." In Computational Optimization Techniques and Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96516.

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Global Optimization in the 4D nonlinear landscape generates kinds and types of particles, waves and extremals of power sets and singletons. In this chapter these are demonstrated for ranges of optimal problem-solving solution algorithms. Here, onts, particles, or atoms, of the ontological blueprint are generated inherently from the fractional optimization algorithms in Metaheuristic structures of computational evolutionary development. These stigmergetics are applicable to incremental machine learning regimes for computational power generation and relay, and information management systems.
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Actas de conferencias sobre el tema "Machine learning. Computational learning theory. Combinatorial optimization"

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Krechetov, Mikhail, Jakub Marecek, Yury Maximov, and Martin Takac. "Entropy-Penalized Semidefinite Programming." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/157.

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Low-rank methods for semi-definite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-based or Schatten-norm penalties, which are difficult to implement in practice due to high computational efforts. In this paper, we propose Entropy-Penalized Semi-Definite Programming (EP-SDP), which provides a unified framework for a broad class of penalty functions used in practice to promote a low-rank solution. We show that EP-SDP problems admit an efficient numerical algorithm, having (almost) linear time comple
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Sharpe, Conner, Clinton Morris, Benjamin Goldsberry, Carolyn Conner Seepersad, and Michael R. Haberman. "Bayesian Network Structure Optimization for Improved Design Space Mapping for Design Exploration With Materials Design Applications." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67643.

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Modern design problems present both opportunities and challenges, including multifunctionality, high dimensionality, highly nonlinear multimodal responses, and multiple levels or scales. These factors are particularly important in materials design problems and make it difficult for traditional optimization algorithms to search the space effectively, and designer intuition is often insufficient in problems of this complexity. Efficient machine learning algorithms can map complex design spaces to help designers quickly identify promising regions of the design space. In particular, Bayesian netwo
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Zennaki, Mahmoud, and Ahmed Ech-cherif. "A New Approach using Machine Learning and Data Fusion Techniques for Solving Hard Combinatorial Optimization Problems." In Communication Technologies: from Theory to Applications (ICTTA). IEEE, 2008. http://dx.doi.org/10.1109/ictta.2008.4530371.

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Li, Dongqin, Philip A. Wilson, Yifeng Guan, and Xin Zhao. "An Effective Approximation Modeling Method for Ship Resistance in Multidisciplinary Ship Design Optimization." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23407.

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Ship design is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional ship design process only involves independent design optimization with some regression formulas within each discipline and there is no guarantee to achieve the optimum design. At the same time, it is crucial to improve the efficiency of modern ship design. Nowadays, the methods of computational fluid dynamics (CFD) has been brought into the ship design optimization. However, there are still some problems such as calculation precision and time consumption especially when CFD s
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Galvan, Edgar, Richard J. Malak, Sean Gibbons, and Raymundo Arroyave. "Constraint Satisfaction Approach to the Design of Multi-Component, Multi-Phase Alloys." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34707.

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The development of new materials must start with an understanding of their phase stability. Researchers have used the CALPHAD method to develop self-consistent databases encoding the thermodynamics of phases. In this forward approach, thermo dynamic conditions (processing conditions such as composition, temperature, pressure, etc.) are mapped to equilibrium states. In this research, we are instead interested in the inverse problem of mapping a set of desired phase constitutions to the set of thermodynamic conditions that give rise to them. Recently, search and optimization techniques have been
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