Relevant bibliographies by topics / MACHINE ALGORITHMS

Academic literature on the topic 'MACHINE ALGORITHMS'

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Published: 11 September 2023

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Journal articles on the topic "MACHINE ALGORITHMS"

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Mishra, Akshansh, and Apoorv Vats. "Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints." Frattura ed Integrità Strutturale 15, no. 58 (September 25, 2021): 242–53. http://dx.doi.org/10.3221/igf-esis.58.18.

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Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms.
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Benbouzid, Bilel. "Unfolding Algorithms." Science & Technology Studies 32, no. 4 (December 13, 2019): 119–36. http://dx.doi.org/10.23987/sts.66156.

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Predictive policing is a research field whose principal aim is to develop machines for predicting crimes, drawing on machine learning algorithms and the growing availability of a diversity of data. This paper deals with the case of the algorithm of PredPol, the best-known startup in predictive policing. The mathematicians behind it took their inspiration from an algorithm created by a French seismologist, a professor in earth sciences at the University of Savoie. As the source code of the PredPol platform is kept inaccessible as a trade secret, the author contacted the seismologist directly in order to try to understand the predictions of the company’s algorithm. Using the same method of calculation on the same data, the seismologist arrived at a different, more cautious interpretation of the algorithm's capacity to predict crime. How were these predictive analyses formed on the two sides of the Atlantic? How do predictive algorithms come to exist differently in these different contexts? How and why is it that predictive machines can foretell a crime that is yet to be committed in a California laboratory, and yet no longer work in another laboratory in Chambéry? In answering these questions, I found that machine learning researchers have a moral vision of their own activity that can be understood by analyzing the values and material consequences involved in the evaluation tests that are used to create the predictions.
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HE, YONG, SHUGUANG HAN, and YIWEI JIANG. "ONLINE ALGORITHMS FOR SCHEDULING WITH MACHINE ACTIVATION COST." Asia-Pacific Journal of Operational Research 24, no. 02 (April 2007): 263–77. http://dx.doi.org/10.1142/s0217595907001231.

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In this paper, we consider a variant of the classical parallel machine scheduling problem. For this problem, we are given m potential identical machines to non-preemptively process a sequence of independent jobs. Machines need to be activated before starting to process, and each machine activated incurs a fixed machine activation cost. No machines are initially activated, and when a job is revealed the algorithm has the option to activate new machines. The objective is to minimize the sum of the makespan and activation cost of machines. We first present two optimal online algorithms with competitive ratios of 3/2 and 5/3 for m = 2, 3 cases, respectively. Then we present an online algorithm with a competitive ratio of at most 2 for general m ≥ 4, while the lower bound is 1.88.
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TURAN, SELIN CEREN, and MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS." Journal of Science and Arts 22, no. 2 (June 30, 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.

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Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared
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Ling, Qingyang. "Machine learning algorithms review." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 91–98. http://dx.doi.org/10.54254/2755-2721/4/20230355.

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Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machine learning and its algorithms. It will examine different types of machine learning models and introduce their most popular algorithms. The methodology of this paper is a literature review, which examines the most commonly used machine learning algorithms in the current field. Such algorithms include Nave Bayes, Decision Tree, KNN, and K-Mean Cluster. Nowadays, machine learning is everywhere and almost everyone using a technology product is enjoying its convenience. Applications like spam mail classification, image recognition, personalized product recommendations, and natural language processing all use machine learning algorithms. The conclusion is that there is no single algorithm that can solve all the problems. The choice of the use of algorithms and models must depend on the specific problem.
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Sameer, S. K. L., and P. Sriramya. "Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease." Alinteri Journal of Agriculture Sciences 36, no. 1 (June 29, 2021): 713–20. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21100.

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Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.
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Meena, Munesh, and Ruchi Sehrawat. "Breakdown of Machine Learning Algorithms." Recent Trends in Artificial Intelligence & it's Applications 1, no. 3 (October 16, 2022): 25–29. http://dx.doi.org/10.46610/rtaia.2022.v01i03.005.

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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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Maitre, Julien, Sébastien Gaboury, Bruno Bouchard, and Abdenour Bouzouane. "A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms." International Journal of Monitoring and Surveillance Technologies Research 3, no. 3 (July 2015): 44–67. http://dx.doi.org/10.4018/ijmstr.2015070103.

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Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.
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Castelo, Noah, Maarten W. Bos, and Donald Lehmann. "Let the Machine Decide: When Consumers Trust or Distrust Algorithms." NIM Marketing Intelligence Review 11, no. 2 (November 1, 2019): 24–29. http://dx.doi.org/10.2478/nimmir-2019-0012.

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AbstractThanks to the rapid progress in the field of artificial intelligence algorithms are able to accomplish an increasingly comprehensive list of tasks, and often they achieve better results than human experts. Nevertheless, many consumers have ambivalent feelings towards algorithms and tend to trust humans more than they trust machines. Especially when tasks are perceived as subjective, consumers often assume that algorithms will be less effective, even if this belief is getting more and more inaccurate.To encourage algorithm adoption, managers should provide empirical evidence of the algorithm’s superior performance relative to humans. Given that consumers trust in the cognitive capabilities of algorithms, another way to increase trust is to demonstrate that these capabilities are relevant for the task in question. Further, explaining that algorithms can detect and understand human emotions can enhance adoption of algorithms for subjective tasks.
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K.M., Umamaheswari. "Road Accident Perusal Using Machine Learning Algorithms." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1676–82. http://dx.doi.org/10.37200/ijpr/v24i5/pr201839.

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Dissertations / Theses on the topic "MACHINE ALGORITHMS"

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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.

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Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.
Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.

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In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
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Moon, Gordon Euhyun. "Parallel Algorithms for Machine Learning." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1561980674706558.

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Roderus, Jens, Simon Larson, and Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.

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The amount of available data has allowed the field of machine learning to flourish. But with growing data set sizes comes an increase in algorithm execution times. Cluster computing frameworks provide tools for distributing data and processing power on several computer nodes and allows for algorithms to run in feasible time frames when data sets are large. Different cluster computing frameworks come with different trade-offs. In this thesis, the scalability of the execution time of machine learning algorithms running on the Hadoop cluster computing framework is investigated. A recent version of Hadoop and algorithms relevant in industry machine learning, namely K-means, latent Dirichlet allocation and naive Bayes are used in the experiments. This paper provides valuable information to anyone choosing between different cluster computing frameworks. The results show everything from moderate scalability to no scalability at all. These results indicate that Hadoop as a framework may have serious restrictions in how well tasks are actually parallelized. Possible scalability improvements could be achieved by modifying the machine learning library algorithms or by Hadoop parameter tuning.
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Sahoo, Shibashankar. "Soft machine : A pattern language for interacting with machine learning algorithms." Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182467.

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The computational nature of soft computing e.g. machine learning and AI systems have been hidden by seamless interfaces for almost two decades now. It has led to the loss of control, inability to explore, and adapt to needs and privacy at an individual level to social-technical problems on a global scale. I propose a soft machine - a set of cohesive design patterns or ‘seams’ to interact with everyday ‘black-box’ algorithms. Through participatory design and tangible sketching, I illustrate several interaction techniques to show how people can naturally control, explore, and adapt in-context algorithmic systems. Unlike existing design approaches, I treat machine learning as playful ‘design material’ finding moments of interplay between human common sense and statical intelligence. Further, I conceive machine learning not as a ‘technology’ but rather as an iterative training ‘process’, which eventually changes the role of user from a passive consumer of technology to an active trainer of algorithms.
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Dunkelberg, Jr John S. "FEM Mesh Mapping to a SIMD Machine Using Genetic Algorithms." Digital WPI, 2001. https://digitalcommons.wpi.edu/etd-theses/1154.

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The Finite Element Method is a computationally expensive method used to perform engineering analyses. By performing such computations on a parallel machine using a SIMD paradigm, these analyses' run time can be drastically reduced. However, the mapping of the FEM mesh elements to the SIMD machine processing elements is an NP-complete problem. This thesis examines the use of Genetic Algorithms as a search technique to find quality solutions to the mapping problem. A hill climbing algorithm is compared to a traditional genetic algorithm, as well as a "messy" genetic algorithm. The results and comparative advantages of these approaches are discussed.
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Williams, Cristyn Barry. "Colour constancy : human mechanisms and machine algorithms." Thesis, City University London, 1995. http://openaccess.city.ac.uk/7731/.

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This thesis describes a quantitative experimental investigation into instantaneous colour constancy in humans. Colour constancy may be defined as the ability of the visual system to maintain a constant colour percept of a surface despite varying conditions of illumination. Instantaneous, in this context, refers to effects which happen very rapidly with the change of illumination, rather than those which may be due to long term adaptation of the photoreceptors. The results of experiments are discussed in the context of current computational models of colour constancy. Experiments on subjects with damage to the cerebral cortex are described. These highlight the different uses of chromatic signals within the cerebral cortex and provide evidence for location of the neural substrates which mediate instantaneous colour constancy. The introductory chapter describes briefly the visual system, in the first section, with particular reference to the processing of colour. The second section discusses the psychophysics of human colour vision and the third presents a summary of computational models of colour constancy described in the literature. Chapter two describes the dynamic colour matching technique developed for this investigation. This technique has the advantage of quantifying the level of constancy achieved, whilst maintaining a constant state of adaptation. The C index is defined as a measure of constancy, with 0 representing no constancy and 1 perfect constancy. Calibration procedures for the computer monitor and the necessary transformations to accurately simulate illuminant reflectance combinations are also described. Light scattered within the eye and its effect on colour constancy are discussed. Chapter three is concerned with the effects of altering the illuminant conditions on instantaneous colour constancy. The size of the illuminant shift is varied. Artificial illuminants are compared with those of the Plankian locus. The effects of overall illuminance and the luminance contrast between target and surround are investigated. Chapter four considers the spatial structure of the visual scene. Simple uniform surrounds are compared with those which have a more complex spatiochromatic structure (Mondrians). The effects of varying the test target size and shape are investigated. The decrease in constancy as a black border is placed between test target and surround is measured. Chapter five describes experiments on four subjects with damage to the cerebral cortex. Chromatic discrimination thresholds are investigated for three subjects with achromatopsia as are the contribution of both sighted and blind hemifields to constancy for a subject with hemianopia. Contrary to the predictions of many of the current computational models, using unnatural illuminants has no substantial effect on the C index, nor does the size of the illuminant shift or the luminance contrast between experimental target and surround. The complexity of the surrounding field does not effect constancy. These findings are similar to those from chromatic induction experiments reported in the literature. However, the effect of a black annulus is found to have different spatial parameters that those reported from experiments on chromatic induction, suggesting that a different mechanism may be involved. The three achromatopsics can be shown to exhibit instantaneous colour constancy. However the blind hemifield of the hemianope does not contribute. This suggests that the fusiform gyrus is not the human homologue of V4 and that the primary visual cortex is necessary for instantaneous colour constancy.
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Mitchell, Brian. "Prepositional phrase attachment using machine learning algorithms." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412729.

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PASSOS, BRUNO LEONARDO KMITA DE OLIVEIRA. "SCHEDULING ALGORITHMS APPLICATION FOR MACHINE AVAILABILITY CONSTRAINT." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24311@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Grande parte da literatura de problemas de escalonamento assume que todas as máquinas estão disponíveis durante todo o período de análise o que, na prática, não é verdade, pois algumas das máquinas podem estar indisponíveis para processamento sem aviso prévio devido a problemas ou a políticas de utilização de seus recursos. Nesta tese, exploramos algumas das poucas heurísticas disponíveis na literatura para a minimização do makespan para este tipo de problema NP-difícil e apresentamos uma nova heurística que utiliza estatísticas de disponibilidade das máquinas para gerar um escalonamento. O estudo experimental com dados reais mostrou que a nova heurística apresenta ganhos de makespan em relação aos demais algoritmos clássicos que não utilizam informações de disponibilidade no processo de decisão. A aplicação prática deste problema está relacionada a precificação de ativos de uma carteira teórica de forma a estabelecer o risco de mercado da forma mais rápida possível através da utilização de recursos tecnológicos ociosos.
Most literature in scheduling theory assumes that machines are always available during the scheduling time interval, which in practice is not true due to machine breakdowns or resource usage policies. We study a few available heuristics for the NP-hard problem of minimizing the makespan when breakdowns may happen. We also develop a new scheduling heuristic based on historical machine availability information. Our experimental study, with real data, suggests that this new heuristic is better in terms of makespan than other algorithms that do not take this information into account. We apply the results of our investigation for the asset-pricing problem of a fund portfolio in order to determine a full valuation market risk using idle technological resources of a company.
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Wen, Tong 1970. "Support Vector Machine algorithms : analysis and applications." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.
Includes bibliographical references (p. 89-97).
Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs solve classification problems by learning from training examples. From the geometry, it is easy to formulate the finding of SVM classifiers as a linearly constrained Quadratic Programming (QP) problem. However, in practice its dual problem is actually computed. An important property of the dual QP problem is that its solution is sparse. The training examples that determine the SVM classifier are known as support vectors (SVs). Motivated by the geometric derivation of the primal QP problem, we investigate how the dual problem is related to the geometry of SVs. This investigation leads to a geometric interpretation of the scaling property of SVMs and an algorithm to further compress the SVs. A random model for the training examples connects the Hessian matrix of the dual QP problem to Wishart matrices. After deriving the distributions of the elements of the inverse Wishart matrix Wn-1(n, nI), we give a conjecture about the summation of the elements of Wn-1(n, nI). It becomes challenging to solve the dual QP problem when the training set is large. We develop a fast algorithm for solving this problem. Numerical experiments show that the MATLAB implementation of this projected Conjugate Gradient algorithm is competitive with benchmark C/C++ codes such as SVMlight and SvmFu. Furthermore, we apply SVMs to time series data.
(cont.) In this application, SVMs are used to predict the movement of the stock market. Our results show that using SVMs has the potential to outperform the solution based on the most widely used geometric Brownian motion model of stock prices.
by Tong Wen.
Ph.D.
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Books on the topic "MACHINE ALGORITHMS"

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Li, Fuwei, Lifeng Lai, and Shuguang Cui. Machine Learning Algorithms. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16375-3.

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Ayyadevara, V. Kishore. Pro Machine Learning Algorithms. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5.

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Arnold, Schönhage. Fast algorithms: A multitape Turing machine implementation. Mannheim: B.I. Wissenschaftsverlag, 1994.

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Whelan, Paul F., and Derek Molloy. Machine Vision Algorithms in Java. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0251-9.

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Grefenstette, John J., ed. Genetic Algorithms for Machine Learning. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2740-4.

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Mandal, Jyotsna Kumar, Somnath Mukhopadhyay, Paramartha Dutta, and Kousik Dasgupta, eds. Algorithms in Machine Learning Paradigms. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1041-0.

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Machine vision: theory, algorithms, practicalities. London: Academic, 1990.

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Davies, E. R. Machine vision: Theory, algorithms, practicalities. 3rd ed. Amsterdam: Elsevier, 2005.

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J, Grefenstette John, ed. Genetic algorithms for machine learning. Boston: Kluwer Academic Publishers, 1994.

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Paliouras, Georgios. Scalability of machine learning algorithms. Manchester: University of Manchester, 1993.

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Book chapters on the topic "MACHINE ALGORITHMS"

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Geetha, T. V., and S. Sendhilkumar. "Classification Algorithms." In Machine Learning, 127–51. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-6.

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Brucker, Peter. "Single Machine Scheduling Problems." In Scheduling Algorithms, 61–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24804-0_4.

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Brucker, Peter. "Single Machine Scheduling Problems." In Scheduling Algorithms, 61–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04550-3_4.

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Brucker, Peter. "Single Machine Scheduling Problems." In Scheduling Algorithms, 61–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03612-9_4.

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Brucker, Peter. "Single Machine Scheduling Problems." In Scheduling Algorithms, 60–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03088-2_4.

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Pendyala, Vishnu. "Machine Learning Algorithms." In Veracity of Big Data, 87–118. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3633-8_5.

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Panesar, Arjun. "Machine Learning Algorithms." In Machine Learning and AI for Healthcare, 119–88. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3799-1_4.

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Steger, Carsten. "Machine Vision Algorithms." In Handbook of Machine and Computer Vision, 505–698. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2017. http://dx.doi.org/10.1002/9783527413409.ch9.

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Panesar, Arjun. "Machine Learning Algorithms." In Machine Learning and AI for Healthcare, 85–144. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6537-6_4.

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Zhou, Ding-Xuan. "Machine Learning Algorithms." In Encyclopedia of Applied and Computational Mathematics, 839–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-540-70529-1_301.

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Conference papers on the topic "MACHINE ALGORITHMS"

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Wang, Yingfeng, Zhijing Liu, and Wei Yan. "Algorithms for Random Adjacency Matrixes Generation Used for Scheduling Algorithms Test." In 2010 International Conference on Machine Vision and Human-machine Interface. IEEE, 2010. http://dx.doi.org/10.1109/mvhi.2010.190.

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Arden, Farel, and Cutifa Safitri. "Hyperparameter Tuning Algorithm Comparison with Machine Learning Algorithms." In 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2022. http://dx.doi.org/10.1109/icitisee57756.2022.10057630.

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Teixeira, L. P., W. Celes, and M. Gattass. "Accelerated Corner-Detector Algorithms." In British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.62.

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Narendra, Pat. "VLSI Architectures for Real-Time Image Processing." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.fd4.

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Real-time image processing has come of age in numerous industrial and military applications. Now is the opportune moment to define architectures which can yield low cost generic VLSI building blocks spanning the various algorithm and system requirements. In this paper, we summarize typical sensor formats and algorithms to identify common data flow and computational structures required for their real-time VLSI implementation. A set of criteria for evaluating architectures for VLSI is developed and used to examine several representative architectures. Recommendations for common VLSI building blocks are made for the representative architectures.
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Shabdirova, Ainash, Ashirgul Kozhagulova, Minh Nguyen, and Yong Zhao. "A Novel Approach to Sand Volume Prediction Using Machine Learning Algorithms." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22770-ea.

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Abstract The objective of the paper is to discuss the application of different Machine Learning (ML) algorithms to predict sand volume during oil production from a weak sandstone reservoir in Kazakhstan. The field data consists of the data set from 10 wells comprising such parameters as fluid flow rate, water cut value, depth of the reservoir, and thickness of the producing zone. Six different algorithms were applied and root-mean-square error (RMSE) was used to compare different algorithms. The algorithms were trained with the data from 8 wells and tested on the data from the other two wells. Variable selection methods were used to identify the most important input parameters. The results show that the KNN algorithm has the best performance. The analysis suggests that the ML algorithm can be successfully used for the prediction of transient and non-transient sand production behavior. The algorithm is especially useful for transient sand production, where sand burst is followed by abrupt decline and finally stops. The results show that the algorithm can fairly predict the peak sand volumes which is useful for sand management measures. The variable selection studies suggest that water cut value and fluid flow rate are the most important parameters both for the sand volume amount and accuracy of the algorithm. The novelty of the paper is an attempt to predict sand volume using ML algorithms while existing studies focused only on sanding onset prediction.
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Halyo, Nesim, and Richard W. Samms. "Combined Optimization of Image Gathering Optics and Image Processing Algorithm for Edge Detection." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.thd1.

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Much attention on edge detection has centered around the development of algorithms which process the sampled image data obtained by an image acquisition system [1]. While considerable work on designing (general-purpose) image gathering optics also exists the interrelationships between the two design processes has received little attention. This paper formulates a combined stochastic optimization problem where the optical transfer function (OTF) of the image gathering optics, the sampling lattice, the noise level and the image processing algorithm are all design parameters to be selected according to the optimization criterion. The optimization criterion used is the minimization of the spatially averaged mean-squared error in estimating a characteristic or a spatial feature related the object scene. Edge detection is treated as a special case of the general problem where the related characteristic contains edge information. This formulation recognizes that the objective is to determine the edges in the object scene rather than the edges of an already blurred and noisy image. As both image acquisition parameters and image processing algorithms can have significant effects on the mean-squared error, the optimization formulated provides an approach to obtain the most compatible acquisition and processing systems.
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Abdullahi, M. I., G. I. O. Aimufua, and U. A. Muhammad. "Application of Sales Forecasting Model Based on Machine Learning Algorithms." In 28th iSTEAMS Multidisciplinary Research Conference AIUWA The Gambia. Society for Multidisciplinary and Advanced Research Techniques - Creative Research Publishers, 2021. http://dx.doi.org/10.22624/aims/isteams-2021/v28p17.

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Machine learning has been a subject undergoing intense study across many different industries and fortunately, companies are becoming gradually more aware of the various machine learning approaches to solve their problems. However, to fully harvest the potential of different machine learning models and to achieve efficient results, one needs to have a good understanding of the application of the models and the nature of data. This paper aims to investigate different approaches to obtain good results of the machine learning algorithms applied for a given forecasting task. To this end, the paper critically analyzes and investigate the applicability of machine learning algorithm in sales forecasting under dynamic conditions, develop a forecasting model based on the regression model, and evaluate the performance of four machine learning regression algorithms (Random Forest, Extreme Gradient Boosting, Support Vector Machine for Regression and Ensemble Model) using data set from Nigeria retail shops for sales forecasting based on performance matrices such as R-squared, Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. Keywords: Sales Forecasting, Model Based, Algorithms Machine Learning
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Courtney, P., R. B. Yates, and P. A. Ivey. "Mapping Algorithms on to Platforms: An Approach to Algorithm and Hardware Co-Design." In British Machine Vision Conference 1994. British Machine Vision Association, 1994. http://dx.doi.org/10.5244/c.8.79.

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Garnica, O. "Finite state machine optimization using genetic algorithms." In Second International Conference on Genetic Algorithms in Engineering Systems. IEE, 1997. http://dx.doi.org/10.1049/cp:19971194.

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Khan, Rehan Ullah, and Saleh Albahli. "Machine Learning Augmentation." In ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3377713.3377726.

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Reports on the topic "MACHINE ALGORITHMS"

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Stepp, Robert E., Bradley L. Whitehall, and Lawrence B. Holder. Toward Intelligent Machine Learning Algorithms. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada197049.

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Caravelli, Francesco. Towards memristor supremacy with novel machine learning algorithms. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1822713.

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Dim, Odera, Carlos Soto, Yonggang Cui, Lap-Yan Cheng, Maia Gemmill, Thomas Grice, Joseph Rivers, Warren Stern, and Michael Todosow. VERIFICATION OF TRISO FUEL BURNUP USING MACHINE LEARNING ALGORITHMS. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1813329.

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Waldrop, Lauren, Carl Hart, Nancy Parker, Chris Pettit, and Scotland McIntosh. Utility of machine learning algorithms for natural background photo classification. Cold Regions Research and Engineering Laboratory (U.S.), June 2018. http://dx.doi.org/10.21079/11681/27344.

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Grechanuk, Pavel, Michael Rising, and Todd Palmer. Application of Machine Learning Algorithms to Identify Problematic Nuclear Data. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1906466.

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Bissett, W. P. Optimizing Machine Learning Algorithms For Hyperspectral Very Shallow Water (VSW) Products. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada531071.

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Bissett, W. P. Optimizing Machine Learning Algorithms for Hyperspectral Very Shallow Water (VSW) Products. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada504929.

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Bissett, W. P. Optimizing Machine Learning Algorithms for Hyperspectral Very Shallow Water (VSW) Products. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada516714.

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Poczos, Barnabas. Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1572709.

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Herrera, Allen, Eugene Moore, and Alexander Heifetz. Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1735365.

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