Relevant bibliographies by topics / Regression based machine learning

Contents

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

Academic literature on the topic 'Regression based machine learning'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Regression based machine learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Regression based machine learning"

1

Yi, Siming. "Walmart Sales Prediction Based on Machine Learning." Highlights in Science, Engineering and Technology 47 (May 11, 2023): 87–94. http://dx.doi.org/10.54097/hset.v47i.8170.

Full text
Abstract:
Accurate sales forecasting can improve a company's profitability while minimizing expenditures. The use of machine learning algorithms to predict product sales has become a hot topic for researchers and companies over the past few years. This report features the machine learning sales prediction model that combines the ML algorithm and meticulous feature engineering processing to predict Walmart sales. The following regressions are analyzed in this paper: linear regression, random forest regression, and XGBoost regression. The regression analysis has been tested for the same time period every year for three years from 2010 to 2012 on a continuous time basis. The experiments show that XGBoost algorithm overperforms the other machine learning methods by examining the same evaluation metric (WAME). The findings can contribute to a better understanding of the development of new decision support for the retail industry e.g., Walmart retail stores. Moreover, this paper also represents a detailed procedure to rank the feature importance for the dataset. Within the next few years, the ML algorithm is destined to become an important approach for business forecasting. However, this strategy largely ignores the time series method for accuracy.
APA, Harvard, Vancouver, ISO, and other styles
2

Zuo, Xiaonan. "Prediction of Facebook and GOOG Prices based on Linear Regression and LSTM Regression." BCP Business & Management 44 (April 27, 2023): 688–95. http://dx.doi.org/10.54691/bcpbm.v44i.4919.

Full text
Abstract:
Stock market analysis is a very difficult task, and stock markets are very complex and constantly changing environments. More and more stock investors are now becoming aware of the prominence of machine learning in the field of stocks and finance, and over the last decade or so machine learning has driven advances in the stock market, such as the ability to use different machine learning methods to predict stock movements in order to arrive at the best decisions and algorithmic trades. The problem that this project wants to investigate is the use of machine learning methods for stock prediction. Two stocks, Facebook and GOOG, were chosen as the datasets for the study. The datasets consisted of stock information from the last decade or so and two machine learning methods, namely long and short term memory and linear regression, were used to make predictions. The results obtained from these two models were analyzing and different results were obtained. The results present the conclusion that the linear regression model is more suitable than the LSTM model for predicting these two groups of stocks. Some error analysis was also carried out and some improvements were given for the two different models.
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Guoqiang, and Peifeng Niu. "An enhanced extreme learning machine based on ridge regression for regression." Neural Computing and Applications 22, no. 3-4 (2011): 803–10. http://dx.doi.org/10.1007/s00521-011-0771-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bodunde.O., Akinyemi, Aladesanmi Temitope.A., Oyebade Adedoyin.I., Aderounmu Ganiyu.A., and Kamagaté Beman.H. "Evaluation of a Bayesian Machine Learning –Based and Regression Analysis -Based Performance Prediction Model for Computer Networks." International Journal of Future Computer and Communication 8, no. 4 (2019): 134–41. http://dx.doi.org/10.18178/ijfcc.2019.8.4.555.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Surendar, S., and M. Elangovan. "Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions During Boring Operation." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (2017): 887. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp887-892.

Full text
Abstract:
Modern manufacturing methods permit the study and prediction of surface roughness since the acquisition of signals and its processing is made instantaneously. With the availability of better computing facilities and newer algorithms in the machine learning domain, online surface roughness prediction will lead to the manufacture of intelligent machines that alert the operator when the process crosses the specified range of roughness. Prediction of surface roughness by multiple linear regression, regression tree and M5P tree methods using multivariable predictors and a single response dependent variable Ra (surface roughness) is attempted. Vibration signal from the boring operation has been acquired for the study that predicts the surface roughness on the inner face of the workpiece. A machine learning approach was used to extract the statistical features and analyzed by four different cases to achieve higher predictability, higher accuracy, low computing effort and reduction of the root mean square error. One case among them was carried out upon feature reduction using Principle Component Analysis (PCA) to examine the effect of feature reduction.
APA, Harvard, Vancouver, ISO, and other styles
6

S., Surendar, and Elangovan M. "Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions during Boring Operation." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (2017): 887–92. https://doi.org/10.11591/ijeecs.v7.i3.pp887-892.

Full text
Abstract:
Modern manufacturing methods permit the study and prediction of surface roughness since the acquisition of signals and its processing is made instantaneously. With the availability of better computing facilities and newer algorithms in the machine learning domain, online surface roughness prediction will lead to the manufacture of intelligent machines that alert the operator when the process crosses the specified range of roughness. Prediction of surface roughness by multiple linear regression, regression tree and M5P tree methods using multivariable predictors and a single response dependent variable Ra (surface roughness) is attempted. Vibration signal from the boring operation has been acquired for the study that predicts the surface roughness on the inner face of the workpiece. A machine learning approach was used to extract the statistical features and analyzed by four different cases to achieve higher predictability, higher accuracy, low computing effort and reduction of the root mean square error. One case among them was carried out upon feature reduction using Principle Component Analysis (PCA) to examine the effect of feature reduction.
APA, Harvard, Vancouver, ISO, and other styles
7

Hafsa, Fathima, Juveria Soha, Fathima Rida, and Hifsa Naaz Syeda. "An Analysis of Car Price Prediction Using Machine Learning." Research and Reviews: Advancement in Cyber Security 2, no. 2 (2025): 33–40. https://doi.org/10.5281/zenodo.15308198.

Full text
Abstract:
<em>Car price prediction is a critical task in the automotive industry, enabling buyers, sellers, and financial institutions to make informed and objective decisions. This research focuses on applying machine learning techniques, specifically Linear Regression and Lasso Regression to predict used car prices based on multiple factors including fuel type, transmission, seller type, vehicle age, and kilometers driven. The dataset was carefully preprocessed to handle missing values and encode categorical variables, ensuring the data was suitable for model training. Both models were evaluated using R&sup2; scores, with Linear Regression achieving high accuracy and Lasso Regression providing a more simplified model by reducing overfitting. The findings demonstrate that even basic regression models can deliver reliable predictions, highlighting the potential of machine learning to improve transparency and efficiency in car pricing within real-world applications.</em>
APA, Harvard, Vancouver, ISO, and other styles
8

Avhishek, Biswas, Talukder Ananya, Bhattacharjee Deep, Chowdhury Arijit, and Sanyal Judhajit. "Machine Learning Based Prediction of Suicide Probability." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 1 (2020): 94–97. https://doi.org/10.35940/ijeat.A1701.1010120.

Full text
Abstract:
Many factors have led to the increase of suicide-proneness in the present era. As a consequence, many novel methods have been proposed in recent times for prediction of the probability of suicides, using different metrics. The current work reviews a number of models and techniques proposed recently, and offers a novel Bayesian machine learning (ML) model for prediction of suicides, involving classification of the data into separate categories. The proposed model is contrasted against similar computationally-inexpensive techniques such as spline regression. The model is found to generate appreciably accurate results for the dataset considered in this work. The application of Bayesian estimation allows the prediction of causation to a greater degree than the standard spline regression models, which is reflected by the comparatively low root mean square error (RMSE) for all estimates obtained by the proposed model.
APA, Harvard, Vancouver, ISO, and other styles
9

Iman, Youssif Ibrahim, and Sedqi Kareem Omar. "Email Spam Classification Based on Logistics Regression." Engineering and Technology Journal 10, no. 05 (2025): 4847–54. https://doi.org/10.5281/zenodo.15378605.

Full text
Abstract:
Email is a common communication tool used by both individuals and organizations. It involves a variety of interactions, including file sharing. In addition to the advantages it provides, there is the uninvited email sharing. This unsolicited email is referred to as "spam." Malicious content like viruses, phishing scams, and unsolicited ads can be found in spam. It is well known that communication security is crucial. In order to filter email systems for malicious tools or software, it is essential to classify them based on a variety of criteria. In these kinds of classification studies, machine learning algorithms work well.&nbsp; The objective of this research is to solve the problem at hand and compare the logistic regression, random forest, naive Bayes decision tree, and support vector machine (SVM) algorithms. The effects of various methods and approaches on the issue were thoroughly examined. A comparison of the various performance outcomes using the various approaches is provided. With an accuracy of 98%, logistic regression was the most accurate, followed by random forest with 97%. &nbsp;
APA, Harvard, Vancouver, ISO, and other styles
10

Vishal, Raja S. V., Kiran Reddy S, B. H. Tippesh, R. Udhand Rahul, Deepak NR Dr., and B. Omprakash. "Machine Learning -Based Stroke Prediction." Journal of Advancement in Parallel Computing 8, no. 2 (2025): 35–42. https://doi.org/10.5281/zenodo.15314873.

Full text
Abstract:
<em>This study emphasizes the importance of early stroke detection and prevention, highlighting challenges like missing and unbalanced data. It evaluates various machine learning models, including Random Forest and Logistic Regression, using k-fold cross-validation on balanced and imbalanced datasets. Key predictors identified are age, BMI, average blood sugar, and marital status. The Random Forest model achieved the highest accuracy (95.5%), demonstrating the potential of machine learning to enhance stroke prediction and improve patient outcomes.</em>
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Regression based machine learning"

1

Thorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.

Full text
Abstract:
Agriculture is becoming more dependent on computerized solutions to make thefarmer’s job easier. The big step that many companies are working towards is fullyautonomous vehicles that work the fields. To that end, the equipment fitted to saidvehicles must also adapt and become autonomous. Making this equipment autonomoustakes many incremental steps, one of which is developing an accurate and reliable tanklevel measurement system. In this thesis, a system for tank level measurement in a seedplanting machine is evaluated. Traditional systems use load cells to measure the weightof the tank however, these types of systems are expensive to build and cumbersome torepair. They also add a lot of weight to the equipment which increases the fuel consump-tion of the tractor. Thus, this thesis investigates the use of radar sensors together witha number of Machine Learning algorithms. Fourteen radar sensors are fitted to a tankat different positions, data is collected, and a preprocessing method is developed. Then,the data is used to test the following Machine Learning algorithms: Bagged RegressionTrees (BG), Random Forest Regression (RF), Boosted Regression Trees (BRT), LinearRegression (LR), Linear Support Vector Machine (L-SVM), Multi-Layer Perceptron Re-gressor (MLPR). The model with the best 5-fold crossvalidation scores was Random For-est, closely followed by Boosted Regression Trees. A robustness test, using 5 previouslyunseen scenarios, revealed that the Boosted Regression Trees model was the most robust.The radar position analysis showed that 6 sensors together with the MLPR model gavethe best RMSE scores.In conclusion, the models performed well on this type of system which shows thatthey might be a competitive alternative to load cell based systems.
APA, Harvard, Vancouver, ISO, and other styles
2

Bagheri, Rajeoni Alireza. "ANALOG CIRCUIT SIZING USING MACHINE LEARNING BASED TRANSISTORCIRCUIT MODEL." University of Akron / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=akron1609428170125214.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Qader, Aso, and William Shiver. "Developing an Advanced Internal Ratings-Based Model by Applying Machine Learning." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273418.

Full text
Abstract:
Since the regulatory framework Basel II was implemented in 2007, banks have been allowed to develop internal risk models for quantifying the capital requirement. By using data on retail non-performing loans from Hoist Finance, the thesis assesses the Advanced Internal Ratings-Based approach. In particular, it focuses on how banks active in the non-performing loan industry, can risk-classify their loans despite limited data availability of the debtors. Moreover, the thesis analyses the effect of the maximum-recovery period on the capital requirement. In short, a comparison of five different mathematical models based on prior research in the field, revealed that the loans may be modelled by a two-step tree model with binary logistic regression and zero-inflated beta-regression, resulting in a maximum-recovery period of eight years. Still it is necessary to recognize the difficulty in distinguishing between low- and high-risk customers by primarily assessing rudimentary data about the borrowers. Recommended future amendments to the analysis in further research would be to include macroeconomic variables to better capture the effect of economic downturns.<br>Sedan det regulatoriska ramverket Basel II implementerades 2007, har banker tillåtits utveckla interna riskmodeller för att beräkna kapitalkravet. Genom att använda data på fallerade konsumentlån från Hoist Finance, utvärderar uppsatsen den avancerade interna riskklassificeringsmodellen. I synnerhet fokuserar arbetet på hur banker aktiva inom sektorn för fallerade lån, kan riskklassificera sina lån trots begränsad datatillgång om låntagarna. Dessutom analyseras effekten av maximala inkassoperioden på kapitalkravet. I sammandrag visade en jämförelse av fem modeller, baserade på tidigare forskning inom området, att lånen kan modelleras genom en tvåstegs trädmodell med logistisk regression samt s.k. zero-inflated beta regression, resulterande i en maximal inkassoperiod om åtta år. Samtidigt är det värt att notera svårigheten i att skilja mellan låg- och högriskslåntagare genom att huvudsakligen analysera elementär data om låntagarna. Rekommenderade tillägg till analysen i fortsatt forskning är att inkludera makroekonomiska variabler för att bättre inkorporera effekten av ekonomiska nedgångar.
APA, Harvard, Vancouver, ISO, and other styles
4

Ekman, Björn. "Machine Learning for Beam Based Mobility Optimization in NR." Thesis, Linköpings universitet, Kommunikationssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136489.

Full text
Abstract:
One option for enabling mobility between 5G nodes is to use a set of area-fixed reference beams in the downlink direction from each node. To save power these reference beams should be turned on only on demand, i.e. only if a mobile needs it. An User Equipment (UE) moving out of a beam's coverage will require a switch from one beam to another, preferably without having to turn on all possible beams to find out which one is the best. This thesis investigates how to transform the beam selection problem into a format suitable for machine learning and how good such solutions are compared to baseline models. The baseline models considered were beam overlap and average Reference Signal Received Power (RSRP), both building beam-to-beam maps. Emphasis in the thesis was on handovers between nodes and finding the beam with the highest RSRP. Beam-hit-rate and RSRP-difference (selected minus best) were key performance indicators and were compared for different numbers of activated beams. The problem was modeled as a Multiple Output Regression (MOR) problem and as a Multi-Class Classification (MCC) problem. Both problems are possible to solve with the random forest model, which was the learning model of choice during this work. An Ericsson simulator was used to simulate and collect data from a seven-site scenario with 40 UEs. Primary features available were the current serving beam index and its RSRP. Additional features, like position and distance, were suggested, though many ended up being limited either by the simulated scenario or by the cost of acquiring the feature in a real-world scenario. Using primary features only, learned models' performance were equal to or worse than the baseline models' performance. Adding distance improved the performance considerably, beating the baseline models, but still leaving room for more improvements.
APA, Harvard, Vancouver, ISO, and other styles
5

Börthas, Lovisa, and Sjölander Jessica Krange. "Machine Learning Based Prediction and Classification for Uplift Modeling." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266379.

Full text
Abstract:
The desire to model the true gain from targeting an individual in marketing purposes has lead to the common use of uplift modeling. Uplift modeling requires the existence of a treatment group as well as a control group and the objective hence becomes estimating the difference between the success probabilities in the two groups. Efficient methods for estimating the probabilities in uplift models are statistical machine learning methods. In this project the different uplift modeling approaches Subtraction of Two Models, Modeling Uplift Directly and the Class Variable Transformation are investigated. The statistical machine learning methods applied are Random Forests and Neural Networks along with the standard method Logistic Regression. The data is collected from a well established retail company and the purpose of the project is thus to investigate which uplift modeling approach and statistical machine learning method that yields in the best performance given the data used in this project. The variable selection step was shown to be a crucial component in the modeling processes as so was the amount of control data in each data set. For the uplift to be successful, the method of choice should be either the Modeling Uplift Directly using Random Forests, or the Class Variable Transformation using Logistic Regression. Neural network - based approaches are sensitive to uneven class distributions and is hence not able to obtain stable models given the data used in this project. Furthermore, the Subtraction of Two Models did not perform well due to the fact that each model tended to focus too much on modeling the class in both data sets separately instead of modeling the difference between the class probabilities. The conclusion is hence to use an approach that models the uplift directly, and also to use a great amount of control data in each data set.<br>Behovet av att kunna modellera den verkliga vinsten av riktad marknadsföring har lett till den idag vanligt förekommande metoden inkrementell responsanalys. För att kunna utföra denna typ av metod krävs förekomsten av en existerande testgrupp samt kontrollgrupp och målet är således att beräkna differensen mellan de positiva utfallen i de två grupperna. Sannolikheten för de positiva utfallen för de två grupperna kan effektivt estimeras med statistiska maskininlärningsmetoder. De inkrementella responsanalysmetoderna som undersöks i detta projekt är subtraktion av två modeller, att modellera den inkrementella responsen direkt samt en klassvariabeltransformation. De statistiska maskininlärningsmetoderna som tillämpas är random forests och neurala nätverk samt standardmetoden logistisk regression. Datan är samlad från ett väletablerat detaljhandelsföretag och målet är därmed att undersöka vilken inkrementell responsanalysmetod och maskininlärningsmetod som presterar bäst givet datan i detta projekt. De mest avgörande aspekterna för att få ett bra resultat visade sig vara variabelselektionen och mängden kontrolldata i varje dataset. För att få ett lyckat resultat bör valet av maskininlärningsmetod vara random forests vilken används för att modellera den inkrementella responsen direkt, eller logistisk regression tillsammans med en klassvariabeltransformation. Neurala nätverksmetoder är känsliga för ojämna klassfördelningar och klarar därmed inte av att erhålla stabila modeller med den givna datan. Vidare presterade subtraktion av två modeller dåligt på grund av att var modell tenderade att fokusera för mycket på att modellera klassen i båda dataseten separat, istället för att modellera differensen mellan dem. Slutsatsen är således att en metod som modellerar den inkrementella responsen direkt samt en relativt stor kontrollgrupp är att föredra för att få ett stabilt resultat.
APA, Harvard, Vancouver, ISO, and other styles
6

Blomkvist, Oscar. "Machine Learning Based Sentiment Classification of Text, with Application to Equity Research Reports." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257506.

Full text
Abstract:
In this thesis, we analyse the sentiment in equity research reports written by analysts at Skandinaviska Enskilda Banken (SEB). We provide a description of established statistical and machine learning methods for classifying the sentiment in text documents as positive or negative. Specifically, a form of recurrent neural network known as long short-term memory (LSTM) is of interest. We investigate two different labelling regimes for generating training data from the reports. Benchmark classification accuracies are obtained using logistic regression models. Finally, two different word embedding models and bidirectional LSTMs of varying network size are implemented and compared to the benchmark results. We find that the logistic regression works well for one of the labelling approaches, and that the best LSTM models outperform it slightly.<br>I denna rapport analyserar vi sentimentet, eller attityden, i aktieanalysrapporter skrivna av analytiker på Skandinaviska Enskilda Banken (SEB). Etablerade statistiska metoder och maskininlärningsmetoder för klassificering av sentimentet i textdokument som antingen positivt eller negativt presenteras. Vi är speciellt intresserade av en typ av rekurrent neuronnät känt som long short-term memory (LSTM). Vidare undersöker vi två olika scheman för att märka upp träningsdatan som genereras från rapporterna. Riktmärken för klassificeringsgraden erhålls med hjälp av logistisk regression. Slutligen implementeras två olika ordrepresentationsmodeller och dubbelriktad LSTM av varierande nätverksstorlek, och jämförs med riktmärkena. Vi finner att logistisk regression presterar bra för ett av märkningsschemana, och att LSTM har något bättre prestanda.
APA, Harvard, Vancouver, ISO, and other styles
7

Faraj, Dina. "Using Machine Learning for Predictive Maintenance in Modern Ground-Based Radar Systems." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299634.

Full text
Abstract:
Military systems are often part of critical operations where unplanned downtime should be avoided at all costs. Using modern machine learning algorithms it could be possible to predict when, where, and at what time a fault is likely to occur which enables time for ordering replacement parts and scheduling maintenance. This thesis is a proof of concept study for anomaly detection in monitoring data, i.e., sensor data from a ground based radar system as an initial experiment to showcase predictive maintenance. The data in this thesis was generated by a Giraffe 4A during normal operation, i.e., no anomalous data with known failures was provided. The problem setting is originally an unsupervised machine learning problem since the data is unlabeled. Speculative binary labels are introduced (start-up state and steady state) to approximate a classification accuracy. The system is functioning correctly in both phases but the monitoring data looks differently. By showing that the two phases can be distinguished, it is possible to assume that anomalous data during break down can be detected as well.  Three different machine learning classifiers, i.e., two unsupervised classifiers, K-means clustering and isolation forest and one supervised classifier, logistic regression are evaluated on their ability to detect the start-up phase each time the system is turned on. The classifiers are evaluated graphically and based on their accuracy score. All three classifiers recognize a start up phase for at least four out of seven subsystems. By only analyzing their accuracy score it appears that logistic regression outperforms the other models. The collected results manifests the possibility to distinguish between start-up and steady state both in a supervised and unsupervised setting. To select the most suitable classifier, further experiments on larger data sets are necessary.<br>Militära system är ofta en del av kritiska operationer där oplanerade driftstopp bör undvikas till varje pris. Med hjälp av moderna maskininlärningsalgoritmer kan det vara möjligt att förutsäga när och var ett fel kommer att inträffa. Detta möjliggör tid för beställning av reservdelar och schemaläggning av underhåll. Denna uppsats är en konceptstudie för detektion av anomalier i övervakningsdata från ett markbaserat radarsystem som ett initialt experiment för att studera prediktivt underhåll. Datat som används i detta arbete kommer från en Saab Giraffe 4A radar under normal operativ drift, dvs. ingen avvikande data med kända brister tillhandahölls. Problemställningen är ursprungligen ett oövervakat maskininlärningsproblem eftersom datat saknar etiketter. Spekulativa binära etiketter introduceras (uppstart och stabil fas) för att uppskatta klassificeringsnoggrannhet. Systemet fungerar korrekt i båda faserna men övervakningsdatat ser annorlunda ut. Genom att visa att de två faserna kan urskiljas, kan man anta att avvikande data också går att detektera när fel uppstår.  Tre olika klassificeringsmetoder dvs. två oövervakade maskininlärningmodeller, K-means klustring och isolation forest samt en övervakad modell, logistisk regression utvärderas utifrån deras förmåga att upptäcka uppstartfasen varje gång systemet slås på. Metoderna utvärderas grafiskt och baserat på deras träffsäkerhet. Alla tre metoderna känner igen en startfas för minst fyra av sju delsystem. Genom att endast analysera deras noggrannhetspoäng, överträffar logistisk regression de andra modellerna. De insamlade resultaten demonstrerar möjligheten att skilja mellan uppstartfas och stabil fas, både i en övervakad och oövervakad miljö. För att välja den bästa metoden är det nödvändigt med ytterligare experiment på större datamängder.
APA, Harvard, Vancouver, ISO, and other styles
8

Li, Xinfeng. "Image based human body rendering via regression & MRF energy minimization." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5188.

Full text
Abstract:
A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks. Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image. Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation. This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method.
APA, Harvard, Vancouver, ISO, and other styles
9

Zambonin, Giuliano. "Development of Machine Learning-based technologies for major appliances: soft sensing for drying technology applications." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3425771.

Full text
Abstract:
In this thesis, Machine Learning techniques for the improvements in the performance of household major appliances are described. In particular, the focus is on drying technologies and domestic dryers are the machines of interest selected as case studies. Statistical models called Soft Sensors have been developed to provide estimates of quantities that are costly/time-consuming to measure in our applications using data that were available for other purposes. The work has been developed as industrially driven research activity in collaborations with Electrolux Italia S.p.a. R&D department located in Porcia, Pordenone, Italy. During the thesis, practical aspects of the implementation of the proposed approaches in a real industrial environment as well as topics related to collaborations between industry and academies are specified.
APA, Harvard, Vancouver, ISO, and other styles
10

Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.

Full text
Abstract:
This thesis has explored the field of internally developed models for measuring the probability of default (PD) in credit risk. As regulators put restrictions on modelling practices and inhibit the advance of risk measurement, the fields of data science and machine learning are advancing. The tradeoff between stricter regulation on internally developed models and the advancement of data analytics was investigated by comparing model performance of the benchmark method Logistic Regression for estimating PD with the machine learning methods Decision Trees, Random Forest, Gradient Boosting and Artificial Neural Networks (ANN). The data was supplied by SEB and contained 45 variables and 24 635 samples. As the machine learning techniques become increasingly complex to favour enhanced performance, it is often at the expense of the interpretability of the model. An exploratory analysis was therefore made with the objective of measuring variable importance in the machine learning techniques. The findings from the exploratory analysis will be compared to the results from benchmark methods that exist for measuring variable importance. The results of this study shows that logistic regression outperformed the machine learning techniques based on the model performance measure AUC with a score of 0.906. The findings from the exploratory analysis did increase the interpretability of the machine learning techniques and were validated by the results from the benchmark methods.<br>Denna uppsats har undersökt internt utvecklade modeller för att estimera sannolikheten för utebliven betalning (PD) inom kreditrisk. Samtidigt som nya regelverk sätter restriktioner på metoder för modellering av kreditrisk och i viss mån hämmar utvecklingen av riskmätning, utvecklas samtidigt mer avancerade metoder inom maskinlärning för riskmätning. Således har avvägningen mellan strängare regelverk av internt utvecklade modeller och framsteg i dataanalys undersökts genom jämförelse av modellprestanda för referens metoden logistisk regression för uppskattning av PD med maskininlärningsteknikerna beslutsträd, Random Forest, Gradient Boosting och artificiella neurala nätverk (ANN). Dataunderlaget kommer från SEB och består utav 45 variabler och 24 635 observationer. När maskininlärningsteknikerna blir mer komplexa för att gynna förbättrad prestanda är det ofta på bekostnad av modellens tolkbarhet. En undersökande analys gjordes därför med målet att mäta förklarningsvariablers betydelse i maskininlärningsteknikerna. Resultaten från den undersökande analysen kommer att jämföras med resultat från etablerade metoder som mäter variabelsignifikans. Resultatet av studien visar att den logistiska regressionen presterade bättre än maskininlärningsteknikerna baserat på prestandamåttet AUC som mätte 0.906. Resultatet from den undersökande analysen för förklarningsvariablers betydelse ökade tolkbarheten för maskininlärningsteknikerna. Resultatet blev även validerat med utkomsten av de etablerade metoderna för att mäta variabelsignifikans.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Regression based machine learning"

1

Diveev, Askhat, and Elizaveta Shmalko. Machine Learning Control by Symbolic Regression. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83213-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Keith, Michael. Machine Learning with Regression in Python. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6583-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kolodner, Janet L. Case-Based Learning. Springer US, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, Han, and Mihaela Cocea. Granular Computing Based Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70058-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Friedman, Craig. Utility-based learning from data. Chapman & Hall/CRC, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tsihrintzis, George A., Maria Virvou, and Lakhmi C. Jain, eds. Advances in Machine Learning/Deep Learning-based Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76794-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yu, Shi, Léon-Charles Tranchevent, Bart De Moor, and Yves Moreau. Kernel-based Data Fusion for Machine Learning. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19406-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wang, Liang, Guoying Zhao, Li Cheng, and Matti Pietikäinen, eds. Machine Learning for Vision-Based Motion Analysis. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-057-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Naidenova, Xenia. Machine learning methods for commonsense reasoning processes: Interactive models. Information Science Reference, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jena, Om Prakash, Sabyasachi Pramanik, and Ahmed A. Elngar. Machine Learning Adoption in Blockchain-Based Intelligent Manufacturing. CRC Press, 2022. http://dx.doi.org/10.1201/9781003252009.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Regression based machine learning"

1

Kalita, Jugal. "Tree-Based Classification and Regression." In Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003002611-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Geetha, T. V., and S. Sendhilkumar. "Probabilistic and Regression Based Approaches." In Machine Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Uther, William, Dunja Mladenić, Massimiliano Ciaramita, et al. "Tree-Based Regression." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_852.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kulkarni, Akshay R., Adarsha Shivananda, Anoosh Kulkarni, and V. Adithya Krishnan. "Machine Learning Regression–based Forecasting." In Time Series Algorithms Recipes. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8978-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Faouzi, Johann, and Olivier Colliot. "Classic Machine Learning Methods." In Machine Learning for Brain Disorders. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3195-9_2.

Full text
Abstract:
AbstractIn this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest neighbor methods, linear and logistic regressions, support vector machines, and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely, for clustering and dimensionality reduction. The chapter does not cover neural networks and deep learning as these will be presented in Chaps. 3, 4, 5, and 6.
APA, Harvard, Vancouver, ISO, and other styles
6

Indurkhya, Nitin, and Sholom M. Weiss. "Rule-Based Ensemble Solutions for Regression." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44596-x_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Chen, Fangjin, Xiangmao Chang, Xiaoxiang Xu, and Yanjun Lu. "RFID Indoor Location Based on Optimized Generalized Regression Neural Network." In Machine Learning and Intelligent Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Moharrer, Armin, Khashayar Kamran, Edmund Yeh, and Stratis Ioannidis. "Robust Regression via Model Based Methods." In Machine Learning and Knowledge Discovery in Databases. Research Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86523-8_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Sugiyama, Masashi, and Shinichi Nakajima. "Pool-Based Agnostic Experiment Design in Linear Regression." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87481-2_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Vitanovski, Dime, Alexey Tsymbal, Razvan Ioan Ionasec, et al. "Accurate Regression-Based 4D Mitral Valve Surface Reconstruction from 2D+t MRI Slices." In Machine Learning in Medical Imaging. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24319-6_35.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Regression based machine learning"

1

Srivastava, Ansh, Mrigaannkaa Singh, and Somesh Nandi. "Support Vector Regression Based Traffic Prediction Machine Learning Model*." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816969.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chang, Xiangwei, Tao Wang, Leyuan Sun, and Peng Hu. "Deep Learning-Based Regression Prediction of Relative Separation Distance of Rockets." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10958107.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

S, Kanagamalliga, and Gayathri VR. "Machine Learning-Based Prediction of Chronic Kidney Disease with Logistic Regression." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013456.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

George, Mary Ann, Anna Merine George, Dattaguru V. Kamath, and Ciji Pearl Kurian. "EV Speed Tracking Using the Regression-Based Supervised Machine Learning Algorithms." In 2024 First International Conference for Women in Computing (InCoWoCo). IEEE, 2024. https://doi.org/10.1109/incowoco64194.2024.10863217.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Girard-Jollet, J., L. Shi, F. Boitier, and P. Layec. "Nonlinearity Estimation Leveraging PSD-based Monitoring and Machine Learning." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m2e.2.

Full text
Abstract:
We propose a regression model to estimate Kerr nonlinearity proportion in fiber optic transmissions. Trained on simulations and validated experimentally, the model achieves a 4% RMSE across varying power profiles, CPE parameters, and transmission reaches.
APA, Harvard, Vancouver, ISO, and other styles
6

Yu, Guo-Xian, Zhi-Wen Yu, Jing Hua, Xuan Li, and Jane You. "Sparse representation based spectral regression." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016791.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Heng-Ru, Fan Min, Dominik Slezak, and Bing Shi. "Cost-sensitive regression-based recommender system." In 2015 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2015. http://dx.doi.org/10.1109/icmlc.2015.7340931.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Salem, Abdel-Badeeh, Rostyslav Yurynets, Zoryna Yurynets, Grzegorz Konieczny, and Paulina Kolisnichenko. "Forecasting the Dynamics of Cryptocurrency Rates Based on Logistic Regression." In Machine Learning Workshop at CoLInS 2024. CoLInS, 2024. http://dx.doi.org/10.31110/colins/2024-1/020.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

James, Aneek E., Alexander Wang, Songli Wang, and Keren Bergman. "Evaluating regression-based techniques for modelling fabrication variations in silicon photonic waveguides." In Applications of Machine Learning 2021, edited by Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2021. http://dx.doi.org/10.1117/12.2594255.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ying Gu, Yan-Yun Qu, and Tian-Zhu Fang. "Image super-resolution based on multikernel regression." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359503.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Regression based machine learning"

1

Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

Full text
Abstract:
Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p&lt;0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p&lt;0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
APA, Harvard, Vancouver, ISO, and other styles
2

de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Banco de España, 2023. http://dx.doi.org/10.53479/33560.

Full text
Abstract:
The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced.
APA, Harvard, Vancouver, ISO, and other styles
3

Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

Full text
Abstract:
A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thesis. Creating a melting snow albedo function and the construction of an aerosol optical depth time series lean on a linear regression approach, whereas the process to fill missing values is based on gradient boosting, a machine learning method that is in turn based on decision trees. These methods reflect the basic nature of these problems as well as the need to take into account the large amounts of satellite-based data and computational resources available.
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2102.

Full text
Abstract:
In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
APA, Harvard, Vancouver, ISO, and other styles
5

Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

Full text
Abstract:
This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.
APA, Harvard, Vancouver, ISO, and other styles
6

Xu, Yuesheng. Adaptive Kernel Based Machine Learning Methods. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada588768.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

List, John, Ian Muir, and Gregory Sun. Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30756.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Trahan, Corey, and Peter Rivera. Scaling and sensitivity analysis of machine learning regression on periodic functions. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47523.

Full text
Abstract:
In this report we document the scalability and sensitivity of machine learning (ML) regression on a periodic, highly oscillating, and 𝐶∞ function. This work is motivated by the need to use ML regression on periodic problems such as tidal propagation. In this work, TensorFlow is used to investigate the machine scalability of a periodic function from one to three dimensions. Wall clock times for each dimension were calculated for a range of layers, neurons, and learning rates to further investigate the sensitivity of the ML regression to these parameters. Lastly, the stochastic gradient descent and Adam optimizers wall clock timings and sensitivities were compared.
APA, Harvard, Vancouver, ISO, and other styles
9

Mishra, Vinod. Subspace Learning Machine (SLM): A New Approach to Classification and Regression. DEVCOM Army Research Laboratory, 2022. http://dx.doi.org/10.21236/ad1183920.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2365.

Full text
Abstract:
The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected and refined comprehensive data from Caltrans project expenditures from 1983 to 2021, ensuring a high-quality dataset. Subject matter experts validated the data, enhancing its reliability. Two models were developed: a statistical model using exponential regression to account for non-linear cost growth, and an AI model employing neural networks to handle complex relationships in the data. Model performance was evaluated based on accuracy and reliability through repeated testing and validation. Key findings indicated that the new models significantly improved the precision of cost forecasts, reducing the variance between predicted and actual project costs. This advancement minimizes budget overruns and enhances resource allocation efficiency. Additionally, leveraging historical data with current market trends refined the models’ predictive power, boosting stakeholder confidence in project budgeting and financial planning. The study’s innovative approach, integrating machine learning and big data analytics, transforms traditional estimation practices and serves as a reference for other state highway programs. Continuous improvement and broader application of these models are recommended to further enhance cost estimation accuracy and support informed decision-making in transportation infrastructure management.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Regression based machine learning' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography