Relevant bibliographies by topics / M5P Algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'M5P Algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "M5P Algorithm"

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Melesse, Assefa M., Khabat Khosravi, John P. Tiefenbacher, Salim Heddam, Sungwon Kim, Amir Mosavi, and Binh Thai Pham. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10 (October 21, 2020): 2951. http://dx.doi.org/10.3390/w12102951.

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Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.
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Debastiani, Aline Bernarda, Sílvio Luís Rafaeli Neto, and Ricardo Dalagnol da Silva. "ÁRVORE MODELO FRENTE A UMA REDE NEURAL ARTIFICIAL PARA A MODELAGEM CHUVA-VAZÃO." Nativa 7, no. 5 (September 12, 2019): 527. http://dx.doi.org/10.31413/nativa.v7i5.7089.

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O objetivo deste estudo é investigar o desempenho da árvore modelo (M5P) e sua sensibilidade à poda e comparação com o desempenho de uma Rede Neural Artificial (RNA) para a simulação da vazão média diária mensal. A motivação para esta análise está na maior simplicidade e velocidade de processamento da M5P comparado às RNAs e a carência de estudos aplicando este método na modelagem hidrológica. O estudo foi desenvolvido na bacia hidrográfica do Alto Canoas, tendo um delineamento experimental composto por um período de treinamento, um de validação cruzada e dois períodos de testes. A RNA utilizada foi a Multi Layer Perceptron (MLP), implementada no software MATLAB, e a M5P (com e sem poda), disponível do software WEKA. O algoritmo M5P se mostrou sensível à poda em somente metade dos tratamentos. A M5P apresentou bom ajuste na modelagem, porém a RNA apresentou desempenho superior em todos os tratamentos.Palavras-chave: rede neural artificial; árvore de regressão; Bacia do Alto Canoas. MODEL TREE IN COMPARISON TO ARTIFICIAL NEURAL NETWORK FOR RAINFALL-RUNOFF MODELING ABSTRACT: The aim of this study is to investigate the performance of the model tree (M5P) and its sensitivity to pruning and comparison to the performance of an Artificial Neural network (ANN) for the simulation of daily average discharge of the month. The motivation for this analysis is on simplicity and speed of processing M5P compared the RNAs. The study was developed in the Alto Canoas watershed, having an experiment consisting of a training period, a cross-validation and two testing periods. The ANN used was the Multi Layer Perceptron (MLP), implemented in MATLAB software, and M5P (with and without pruning), available from the WEKA software. M5P algorithm proved sensitive to pruning in half of the treatments. The M5P showed good fit in the modeling, but the RNA presented superior performance in all treatments.Keywords: artificial neural network; regression tree; Basin Alto Canoas.
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Thai, Thi Huyen, Richard Ansong Omari, Dietmar Barkusky, and Sonoko Dorothea Bellingrath-Kimura. "Statistical Analysis versus the M5P Machine Learning Algorithm to Analyze the Yield of Winter Wheat in a Long-Term Fertilizer Experiment." Agronomy 10, no. 11 (November 13, 2020): 1779. http://dx.doi.org/10.3390/agronomy10111779.

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To compare how different analytical methods explain crop yields from a long-term field experiment (LTFE), we analyzed the grain yield of winter wheat (WW) under different fertilizer applications in Müncheberg, Germany. An analysis of variance (ANOVA), linear mixed-effects model (LMM), and MP5 regression tree model were used to evaluate the grain yield response. All the methods identified fertilizer application and environmental factors as the main variables that explained 80% of the variance in grain yields. Mineral nitrogen fertilizer (NF) application was the major factor that influenced the grain yield in all methods. Farmyard manure slightly influenced the grain yield with no NF application in the ANOVA and M5P regression tree. While sources of environmental factors were unmeasured in the ANOVA test, they were quantified in detail in the LMM and M5P model. The LMM and M5P model identified the cumulative number of freezing days in December as the main climate-based determinant of the grain yield variation. Additionally, the temperature in October, the cumulative number of freezing days in February, the yield of the preceding crop, and the total nitrogen in the soil were determinants of the grain yield in both models. Apart from the common determinants that appeared in both models, the LMM additionally showed precipitation in June and the cumulative number of days in July with temperatures above 30 °C, while the M5P model showed soil organic carbon as an influencing factor of the grain yield. The ANOVA results provide only the main factors affecting the WW yield. The LMM had a better predictive performance compared to the M5P, with smaller root mean square and mean absolute errors. However, they were richer regressors than the ANOVA. The M5P model presented an intuitive visualization of important variables and their critical thresholds, and revealed other variables that were not captured by the LMM model. Hence, the use of different methods can strengthen the statement of the analysis, and thus, the co-use of the LMM and M5P model should be considered, especially in large databases involving multiple variables.
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Kazllarof, Vangjel, Stamatis Karlos, and Sotiris Kotsiantis. "Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks." Informatics 7, no. 4 (November 3, 2020): 50. http://dx.doi.org/10.3390/informatics7040050.

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Active learning is the category of partially supervised algorithms that is differentiated by its strategy to combine both the predictive ability of a base learner and the human knowledge so as to exploit adequately the existence of unlabeled data. Its ambition is to compose powerful learning algorithms which otherwise would be based only on insufficient labelled samples. Since the latter kind of information could raise important monetization costs and time obstacles, the human contribution should be seriously restricted compared with the former. For this reason, we investigate the use of the Logitboost wrapper classifier, a popular variant of ensemble algorithms which adopts the technique of boosting along with a regression base learner based on Model trees into 3 different active learning query strategies. We study its efficiency against 10 separate learners under a well-described active learning framework over 91 datasets which have been split to binary and multi-class problems. We also included one typical Logitboost variant with a separate internal regressor for discriminating the benefits of adopting a more accurate regression tree than one-node trees, while we examined the efficacy of one hyperparameter of the proposed algorithm. Since the application of the boosting technique may provide overall less biased predictions, we assume that the proposed algorithm, named as Logitboost(M5P), could provide both accurate and robust decisions under active learning scenarios that would be beneficial on real-life weakly supervised classification tasks. Its smoother weighting stage over the misclassified cases during training as well as the accurate behavior of M5P are the main factors that lead towards this performance. Proper statistical comparisons over the metric of classification accuracy verify our assumptions, while adoption of M5P instead of weak decision trees was proven to be more competitive for the majority of the examined problems. We present our results through appropriate summarization approaches and explanatory visualizations, commenting our results per case.
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Zhan, Chengjun, Albert Gan, and Mohammed Hadi. "Prediction of Lane Clearance Time of Freeway Incidents Using the M5P Tree Algorithm." IEEE Transactions on Intelligent Transportation Systems 12, no. 4 (December 2011): 1549–57. http://dx.doi.org/10.1109/tits.2011.2161634.

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Kandiri, Amirreza, Farid Sartipi, and Mahdi Kioumarsi. "Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms." Applied Sciences 11, no. 2 (January 6, 2021): 485. http://dx.doi.org/10.3390/app11020485.

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Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.
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Behnood, Ali, Venous Behnood, Mahsa Modiri Gharehveran, and Kursat Esat Alyamac. "Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm." Construction and Building Materials 142 (July 2017): 199–207. http://dx.doi.org/10.1016/j.conbuildmat.2017.03.061.

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Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (September 2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.

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The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.
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Behnood, Ali, and Dana Daneshvar. "A machine learning study of the dynamic modulus of asphalt concretes: An application of M5P model tree algorithm." Construction and Building Materials 262 (November 2020): 120544. http://dx.doi.org/10.1016/j.conbuildmat.2020.120544.

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Liu, Zhao, Jin-sheng Yang, Yuan Wu, Ou Zhang, Min Chen, Ling-ling Huang, Xiu-qing He, Guan-yi Wu, and Ying-ying Wang. "Predictors for Smoking Cessation with Acupuncture in a Hong Kong Population." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/189694.

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Background. Observational studies of smoking cessation with acupuncture have been reported widely; however, few researchers have focused on its predictors.Objective. This paper attempts to explore the predictors for smoking cessation with acupuncture in a Hong Kong population, aiming to provide references for clinical treatment in the future.Methods. We performed a secondary analysis of data from our observational study “Acupuncture for Smoking Cessation (2011–2014)” in Hong Kong. A total of 23 indexes were selected as possible predictors, and study participants with complete information of 23 indexes were included. By taking 8-week and 52-week smoking cessation results as dependent variables, binary logistic regression method was used to identify the predictors. Additionally, based on an M5P decision-tree algorithm, an equation of “successful rate of smoking cessation with acupuncture” was calculated.Results. (1) 2,051 study participants were included in total. (2) According to the results of binary logistic regression, variables including treatment location, total number of acupuncture sessions received, and whether the study participants received at least 6 sessions of acupuncture were taken as the short-term predictors; gender, treatment location, Fagerstrom Test for Nicotine Dependence (FTND), and total number of acupuncture sessions received were taken as the long-term predictors. (3) According to study participants’ FTND, treatment location, and number of cigarettes smoked/day, the equation of “successful rate of smoking cessation with acupuncture” was established.Conclusion. Receiving sufficient and qualified acupuncture is the leading factor for short-term smoking cessation with acupuncture, whereas individual factors and smoking background play a more important role in long-term smoking cessation with acupuncture.
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Dissertations / Theses on the topic "M5P Algorithm"

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Rodríguez, Elen Yanina Aguirre. "Técnicas de aprendizado de máquina para predição do custo da logística de transporte : uma aplicação em empresa do segmento de autopeças /." Guaratinguetá, 2020. http://hdl.handle.net/11449/192326.

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Orientador: Fernando Augusto Silva Marins
Resumo: Em diferentes aspectos da vida cotidiana, o ser humano é forçado a escolher entre várias opções, esse processo é conhecido como tomada de decisão. No nível do negócio, a tomada de decisões desempenha um papel muito importante, porque dessas decisões depende o sucesso ou o fracasso das organizações. No entanto, em muitos casos, tomar decisões erradas pode gerar grandes custos. Desta forma, alguns dos problemas de tomada de decisão que um gerente enfrenta comumente são, por exemplo, a decisão para determinar um preço, a decisão de comprar ou fabricar, em problemas de logística, problemas de armazenamento, etc. Por outro lado, a coleta de dados tornou-se uma vantagem competitiva, pois pode ser utilizada para análise e extração de resultados significativos por meio da aplicação de diversas técnicas, como estatística, simulação, matemática, econometria e técnicas atuais, como aprendizagem de máquina para a criação de modelos preditivos. Além disso, há evidências na literatura de que a criação de modelos com técnicas de aprendizagem de máquina têm um impacto positivo na indústria e em diferentes áreas de pesquisa. Nesse contexto, o presente trabalho propõe o desenvolvimento de um modelo preditivo para tomada de decisão, usando as técnicas supervisionadas de aprendizado de máquina, e combinando o modelo gerado com as restrições pertencentes ao processo de otimização. O objetivo da proposta é treinar um modelo matemático com dados históricos de um processo decisório e obter os predit... (Resumo completo, clicar acesso eletrônico abaixo)
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Chapala, Usha Kiran, and Sridhar Peteti. "Continuous Video Quality of Experience Modelling using Machine Learning Model Trees." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 1996. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17814.

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Adaptive video streaming is perpetually influenced by unpredictable network conditions, whichcauses playback interruptions like stalling, rebuffering and video bit rate fluctuations. Thisleads to potential degradation of end-user Quality of Experience (QoE) and may make userchurn from the service. Video QoE modelling that precisely predicts the end users QoE underthese unstable conditions is taken into consideration quickly. The root cause analysis for thesedegradations is required for the service provider. These sudden changes in trend are not visiblefrom monitoring the data from the underlying network service. Thus, this is challenging toknow this change and model the instantaneous QoE. For this modelling continuous time, QoEratings are taken into consideration rather than the overall end QoE rating per video. To reducethe user risk of churning the network providers should give the best quality to the users. In this thesis, we proposed the QoE modelling to analyze the user reactions change over timeusing machine learning models. The machine learning models are used to predict the QoEratings and change patterns in ratings. We test the model on video Quality dataset availablepublicly which contains the user subjective QoE ratings for the network distortions. M5P modeltree algorithm is used for the prediction of user ratings over time. M5P model gives themathematical equations and leads to more insights by given equations. Results of the algorithmshow that model tree is a good approach for the prediction of the continuous QoE and to detectchange points of ratings. It is shown that to which extent these algorithms are used to estimatechanges. The analysis of model provides valuable insights by analyzing exponential transitionsbetween different level of predicted ratings. The outcome provided by the analysis explains theuser behavior when the quality decreases the user ratings decrease faster than the increase inquality with time. The earlier work on the exponential transitions of instantaneous QoE overtime is supported by the model tree to the user reaction to sudden changes such as video freezes.
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Sehovic, Mirsad, and Markus Carlsson. "Nåbarhetstestning i en baneditor : En undersökning i hur nåbarhetstester kan implementeras i en baneditor samt funktionens potential i att ersätta manuell testning." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-36394.

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Denna studie undersöker om det är möjligt att införa nåbarhetstestning i en baneditor. Testets syfte är att ersätta manuell testing, det vill säga att bankonstruktören inte ska behöva spela igenom banan för att säkerställa att denne kommer kunna nå alla nåbara positioner.För att kunna utföra studien skapas en enkel baneditor som testplattform. Vidare utförs en jämförande studie av flera alternativa algoritmer för att fastställa vilken som är mest passande för nåbarhetstestning i en baneditor.Resultatet från den jämförande studien visade att A* (A star) var den mest passande algoritmen för funktionen. Huruvida automatisk testning kan ersätta manuell testning är diskutabelt, men resultatet pekar på en ökad effektivitet i tid när det kommer till banbygge.
The following study examines whether it is possible to implement reachability testing in a map editor designed for 2D-platform games. The purpose of reachability testing is to replace manual testing, that being the level designer having to play through the map just to see if the player can reach all supposedly reachable positions in the map.A simple map editor is created to enable the implementation after which we perform a theoretical study in order to determine which algorithm would be best suited for the implementation of the reachability testing.The results comparing algorithms shows that A* (A star) worked best with the function. Whether or not manual testing can be replaced by automatic testing is open for debate, however the results points to an increase in time efficiency when it comes to level design.
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Jiang, Minghui. "Map labeling with circles." Diss., Montana State University, 2005. http://etd.lib.montana.edu/etd/2005/jiang/JiangM0505.pdf.

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Carrigan, Braxton Bezdek András. "Evading triangles without a map." Auburn, Ala., 2010. http://hdl.handle.net/10415/2032.

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Hislop, A. D. "Parallel algorithms for digital map path optimisation." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315321.

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Yuen, Patrick Wingkee. "Applying modified CLEAN algorithm to MAP image super-resolution." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187279.

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In this dissertation, the super-resolution method that we use for image restoration is the Poisson Maximum A-Posteriori (MAP) super-resolution algorithm of Hunt, computed with an iterative form. This algorithm is similar to the Maximum Likelihood of Holmes, which is derived from an Expectation/Maximization (EM) computation. Image restoration of point source data is our focus. This is because most astronomical data can be regarded as multiple point source data with a very dark background. The statistical limits imposed by photon noise on the resolution obtained by our algorithm are investigated. We improve the performance of the super-resolution algorithm by including the additional information of the spatial constraints. This is achieved by applying the well-known CLEAN algorithm, which is widely used in astronomy, to create regions of support for the potential point sources. Real and simulated data are included in this paper. The point spread function (psf) of a diffraction limited optical system is used for the simulated data. The real data is two dimensional optical image data from the Hubble Space Telescope.
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Sutton, David William Peter. "Map-making algorithms in future CMB polarisation experiments." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540274.

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Rankenburg, Ivan. "Application of the difference map algorithm to protein structure prediction." Saarbrücken VDM Verlag Dr. Müller, 2007. http://d-nb.info/991338103/04.

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Li, Xiaoli. "A map-growing localization algorithm for ad-hoc sensor networks /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418044.

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Books on the topic "M5P Algorithm"

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. Map Construction Algorithms. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0.

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Coloring mixed hypergraphs: Theory, algorithms and applications. Providence, R.I: American Mathematical Society, 2002.

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Athar, M. An investigation into map-matching algorithms forautomobilenavigation systems. Manchester: UMIST, 1993.

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Depth map and 3D imaging applications: Algorithms and technologies. Hershey, PA: Information Science Reference, 2012.

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Sanchez, Gustavo, Luciano Agostini, and César Marcon. Algorithms for Efficient and Fast 3D-HEVC Depth Map Encoding. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-25927-3.

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Yan, Haowen. Description Approaches and Automated Generalization Algorithms for Groups of Map Objects. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3678-2.

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Hulse, Paul. Applying genetic algorithms to importance map generation for Monte Carlo tracking shielding calculations. Salford: University of Salford, 1994.

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Sahai, A. K. An objective study of Indian summer monsoon variability using the self organizing map algorithms. Pune: Indian Institute of Tropical Meteorology, 2006.

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A, Reed Bruce, ed. Graph colouring and the probabilistic method. Berlin: Springer, 2002.

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Pfoser, Dieter, Mahmuda Ahmed, Sophia Karagiorgou, and Carola Wenk. Map Construction Algorithms. Springer, 2019.

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Book chapters on the topic "M5P Algorithm"

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "TraceBundle Map Construction Algorithm." In Map Construction Algorithms, 15–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_2.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Map Construction Algorithms." In Map Construction Algorithms, 1–14. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_1.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Fréchet Distance-Based Map Construction Algorithm." In Map Construction Algorithms, 33–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_3.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Density-Based Map Construction Pipeline." In Map Construction Algorithms, 47–55. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_4.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Datasets." In Map Construction Algorithms, 57–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_5.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Quality Measures for Map Comparison." In Map Construction Algorithms, 71–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_6.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Evaluation." In Map Construction Algorithms, 85–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_7.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "New Directions." In Map Construction Algorithms, 99–109. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_8.

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Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Resources." In Map Construction Algorithms, 111–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_9.

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Gamrath, Gerald, Benjamin Hiller, and Jakob Witzig. "Reoptimization Techniques for MIP Solvers." In Experimental Algorithms, 181–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20086-6_14.

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Conference papers on the topic "M5P Algorithm"

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Fiedler, Markus. "Performance Analytics by Means of the M5P Machine Learning Algorithm." In 2019 31st International Teletraffic Congress (ITC 31). IEEE, 2019. http://dx.doi.org/10.1109/itc31.2019.00023.

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Sakhrawi, Zaineb, Asma Sellami, and Nadia Bouassida. "An Improved Prediction of Software Enhancement Effort using Correlation-Based Feature Selection and M5P ML Algorithm." In 2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2020. http://dx.doi.org/10.1109/aiccsa50499.2020.9316495.

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Gupta, Anchit, and Shivaram Kalyanakrishnan. "Improved Strong Worst-case Upper Bounds for MDP Planning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/248.

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The Markov Decision Problem (MDP) plays a central role in AI as an abstraction of sequential decision making. We contribute to the theoretical analysis of MDP PLANNING, which is the problem of computing an optimal policy for a given MDP. Specifically, we furnish improved STRONG WORST-CASE upper bounds on the running time of MDP planning. Strong bounds are those that depend only on the number of states n and the number of actions k in the specified MDP; they have no dependence on affiliated variables such as the discount factor and the number of bits needed to represent the MDP. Worst-case bounds apply to EVERY run of an algorithm; randomised algorithms can typically yield faster EXPECTED running times. While the special case of 2-action MDPs (that is, k = 2) has recently received some attention, bounds for general k have remained to be improved for several decades. Our contributions are to this general case. For k >= 3, the tightest strong upper bound shown to date for MDP planning belongs to a family of algorithms called Policy Iteration. This bound is only a polynomial improvement over a trivial bound of poly(n, k) k^{n} [Mansour and Singh, 1999]. In this paper, we generalise a contrasting algorithm called the Fibonacci Seesaw, and derive a bound of poly(n, k) k^{0.6834n}. The key construct we use is a template to map algorithms for the 2-action setting to the general setting. Interestingly, this idea can also be used to design Policy Iteration algorithms with a running time upper bound of poly(n, k) k^{0.7207n}. Both our results improve upon bounds that have stood for several decades.
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Fang, Jian-Jun, Karthik Ramaswamy, Kathy Moseler, Sami Levi, and Sethuraman Panchanathan. "Complexity of the MIP map algorithm for perspective texture warping." In Electronic Imaging, edited by Sethuraman Panchanathan, V. Michael Bove, Jr., and Subramania I. Sudharsanan. SPIE, 1999. http://dx.doi.org/10.1117/12.375235.

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Portugal, David, and Rui Rocha. "MSP algorithm." In the 2010 ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774360.

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Jiang, Hua, Dongming Zhu, Zhichao Xie, Shaowen Yao, and Zhang-Hua Fu. "A New Upper Bound Based on Vertex Partitioning for the Maximum K-plex Problem." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/233.

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Given an undirected graph, the Maximum k-plex Problem (MKP) is to find a largest induced subgraph in which each vertex has at most k−1 non-adjacent vertices. The problem arises in social network analysis and has found applications in many important areas employing graph-based data mining. Existing exact algorithms usually implement a branch-and-bound approach that requires a tight upper bound to reduce the search space. In this paper, we propose a new upper bound for MKP, which is a partitioning of the candidate vertex set with respect to the constructing solution. We implement a new branch-and-bound algorithm that employs the upper bound to reduce the number of branches. Experimental results show that the upper bound is very effective in reducing the search space. The new algorithm outperforms the state-of-the-art algorithms significantly on real-world massive graphs, DIMACS graphs and random graphs.
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Cai, Shaowei, Wenying Hou, Jinkun Lin, and Yuanjie Li. "Improving Local Search for Minimum Weight Vertex Cover by Dynamic Strategies." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/196.

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The minimum weight vertex cover (MWVC) problem is an important combinatorial optimization problem with various real-world applications. Due to its NP hardness, most works on solving MWVC focus on heuristic algorithms that can return a good quality solution in reasonable time. In this work, we propose two dynamic strategies that adjust the behavior of the algorithm during search, which are used to improve a state of the art local search for MWVC named FastWVC, resulting in two local search algorithms called DynWVC1 and DynWVC2. Previous MWVC algorithms are evaluated on graphs with random or hand crafted weights. In this work, we evaluate the algorithms on the vertex weighted graphs that obtained from an important real world problem, the map labeling problem. Experiments show that our algorithm obtains better results than previous algorithms for MWVC and maximum weight independent set (MWIS) on these real world instances. We also test our algorithms on massive graphs studied in previous works, and show significant improvements there.
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Feraco, Stefano, Angelo Bonfitto, Irfan Khan, Nicola Amati, and Andrea Tonoli. "Optimal Trajectory Generation Using an Improved Probabilistic Road Map Algorithm for Autonomous Driving." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22311.

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Abstract This paper presents a technique based on the probabilistic road map algorithm for trajectory planning in autonomous driving. The objective is to provide an algorithm allowing to compute the trajectory of the vehicle by reducing the distance traveled and minimizing the lateral deviation and relative yaw angle of the vehicle with respect to the reference trajectory, while maximizing its longitudinal speed. The vehicle is considered as a 3 Degree-of-Freedom bicycle model and a Model Predictive Control algorithm is implemented to control the lateral and longitudinal dynamics. Both the control and trajectory generation algorithms take the road lane boundaries as the only input from the surrounding environment exploiting a simulated camera. The performance of the technique is compared with the case in which the reference trajectory is the central line between the lane boundaries. The proposed algorithm is validated in a simulated driving scenario.
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Chen, Yuekun, and Yousef Sardahi. "Multi-Objective Optimal Design of an Active Aeroelastic Cascade Control System for an Aircraft Wing With a Leading and Trailing Control Surface." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3121.

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Abstract This paper presents a multi-objective optimal design of cascade controllers applied to an aircraft wing with a leading and trailing control surface driven by electromagnetic actuators (EMAs). The design of the control system is decoupled into an inner (slave or secondary) and outer (master or primary) control algorithm. The master control algorithm is applied to the dynamics of the wing and its ailerons while two salve control loops are designed for the two EMAs. Then, a multi-objective and optimal design of the control algorithms is carried out. Three objectives are considered : 1) the speed of response of the slave controlled system must be faster than that of the master one, 2) the controlled system must be robust against external upsets, and 3) optimal energy consumption. The multi-objective optimization problem (MOP) is solved by the non-dominated sorting genetic algorithm (NSGA-II), which is one of the widely algorithms in solving MOPs. The setup parameters of the primary and secondary control algorithms are tuned during the optimization and the design objectives are evaluated. The solution of the MOP is a set of optimal cascade controllers that represent the trade-offs among the design objectives. Computer simulations show that the design objectives are achieved. However, some of the optimal solutions are practically in-feasible because they respond poorly to external disturbances. Presented study may become the basis for multi-objective optimal design of active aeroelastic control systems.
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Iman Taheri, Seyed, Lucas Lima Rodrigues, Mauricio B. C. Salles, and Alfeu Joãozinho Sguarezi Filho. "A day-ahead hybrid optimization algorithm for finding the dispatch schedule of VPP in a distribution system." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2476.

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Distributed renewable generations such as photovoltaic units are electricity generators for installing close to the loads on the distribution system. In this paper, the dispatch function of a non-centralized Virtual Power Plant (VPP) with having a photovoltaic unit in each bus is considered to optimize. This dispatch function is assigned based on the predicted load shape of the next day. A new day-ahead hybrid optimization algorithm is presented to optimize the dispatch function. The proposed algorithm implements a new hybrid combination of Particle Swarm Optimization (PSO) and Genetic Optimization (GA) algorithms simultaneously to benefit both algorithms’ advantages. The objective function is the optimization of the voltage deviation of the VPP. The suggested algorithm is executed on a 13-bus-radial IEEE standard VPP system using MATLAB software coupled with open-source software called Open-DSS. The results show the importance of the proposed algorithm to optimize the voltage deviation of the VPP. The superiority of the proposed algorithm is related to the accuracy and calculation velocity in comparison with the other tested evolutionary algorithms. The Distribution System Operator could map and move towards its full benefits of the increasing integration of DGs with a strategic placement that could keen prosumers on integrating these actions.
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Reports on the topic "M5P Algorithm"

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Yong, Younghoon. Modified algorithm for building an employment map. Web of Open Science, July 2020. http://dx.doi.org/10.37686/asr.v1i1.64.

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Author, Not Given. An optimal MAP classification algorithm for color printed pattern evaluation. Office of Scientific and Technical Information (OSTI), September 1995. http://dx.doi.org/10.2172/10129767.

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Close, E. R., D. W. Merrill, and H. H. Holmes. Implementation of a new algorithm for Density Equalizing Map Projections (DEMP). Office of Scientific and Technical Information (OSTI), July 1995. http://dx.doi.org/10.2172/110708.

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S.R. Harker, C.R. Vogel, and T. Gedeon. Analysis of Constrained Optimization Variants of the Map-Seeking Circuit Algorithm. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/891009.

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Brower, J. C., and D. F. Merriam. Geological map analysis and comparison by several multivariate algorithms. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128055.

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Ghosh Roy, D. N., K. Wilton, T. A. Cook, S. Chakrabarti, J. Qi, and G. T. Gullberg. Tomographic reconstructions using map algorithms - application to the SPIDR mission. Office of Scientific and Technical Information (OSTI), January 2004. http://dx.doi.org/10.2172/838183.

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Tsoupas, Nicholaos. Algorithm to calculate off-plane magnetic field from an on-plane field map. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1440354.

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Calahan, D. A. Conflict Sensitivity of Algorithms. Part 1. A CRAY X-MP Study. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada160206.

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Attaway, S. W., M. E. Davis, M. W. Heinstein, and J. S. Swegle. Optimizing the Point-In-Box Search Algorithm for the Cray Y-MP(TM) Supercomputer. Office of Scientific and Technical Information (OSTI), December 1998. http://dx.doi.org/10.2172/2603.

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Sinclair, Samantha, and Sandra LeGrand. Reproducibility assessment and uncertainty quantification in subjective dust source mapping. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41523.

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Accurate dust-source characterizations are critical for effectively modeling dust storms. A previous study developed an approach to manually map dust plume-head point sources in a geographic information system (GIS) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery processed through dust-enhancement algorithms. With this technique, the location of a dust source is digitized and recorded if an analyst observes an unobscured plume head in the imagery. Because airborne dust must be sufficiently elevated for overland dust-enhancement algorithms to work, this technique may include up to 10 km in digitized dust-source location error due to downwind advection. However, the potential for error in this method due to analyst subjectivity has never been formally quantified. In this study, we evaluate a version of the methodology adapted to better enable reproducibility assessments amongst multiple analysts to determine the role of analyst subjectivity on recorded dust source location error. Four analysts individually mapped dust plumes in Southwest Asia and Northwest Africa using five years of MODIS imagery collected from 15 May to 31 August. A plume-source location is considered reproducible if the maximum distance between the analyst point-source markers for a single plume is ≤10 km. Results suggest analyst marker placement is reproducible; however, additional analyst subjectivity-induced error (7 km determined in this study) should be considered to fully characterize locational uncertainty. Additionally, most of the identified plume heads (> 90%) were not marked by all participating analysts, which indicates dust source maps generated using this technique may differ substantially between users.
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