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Testing and Quality Assurance
This segment delves into the critical significance of systematic testing during the implementation of Business Central, emphasizing the urgency of...
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Latent Block Regression Model
When dealing with high dimensional sparse data, such as in recommender systems,co-clusteringturnsouttobemorebeneficialthanone-sidedclustering,even if...
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Two improving approaches for faulty interaction localization using logistic regression analysis
Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial...
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Supervised Learning: Classification and Regression
This chapter provides an overview and evaluation of Online Machine Learning (OML) methods and algorithms, with a special focus on supervised...
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Designing Robust Regression Models
In this study we focus on the preference among competing models from a family of polynomial regressors. Classical statistics offers a number of...
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Regression
Regression estimates the relationship between dependent variables and independent variables. Linear regression is an easily understood, popular basic...
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Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly....
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Prediction of California bearing ratio using hybrid regression models
CBR assesses the subgrade strength of road infrastructure, typically requiring time-consuming laboratory testing. In this study, Machine Learning...
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Tri-level regression testing using nature-inspired algorithms
A software needs to be updated to survive in the customers’ ever-changing demands and the competitive market. The modifications may produce...
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Sparse Graph Hashing with Spectral Regression
Learning-based hashing has received increasing research attention due to its promising efficiency for large-scale similarity search. However, most...
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Diversified deep hierarchical kernel ensemble regression
Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be...
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Robust Losses in Deep Regression
What is the noise distribution of a given regression problem is not known in advance and, given that the assumption on which noise is present is...
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Chapter 1 Fundamentals of Testing
This chapter defines what is meant by “testing”; describes the related principles, activities, and roles; and lists essential skills and best...
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Decouple and align classification and regression in one-stage object detection
Current one-stage object detection methods use dense prediction to generate classification and regression results at the same point on the feature...
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Twin Support Vector Regression with Privileged Information
In this paper, we propose a novel framework called Twin Support Vector Regression with Privileged Information (TSVR+), which aims to improve the...
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Integrated Classification and Regression for Quantitative Biomarker Analysis in Mixture Samples
This study aims to develop a comprehensive approach for the classification and concentration estimation of multiple target analytes in biosensing...
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Linear Regression
In statistics, linear regression is a linear method to model the relationship between a scalar response (dependent variable), y, and one or more...
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On the value of parameter tuning in stacking ensemble model for software regression test effort estimation
A type of software testing, regression testing is often costly and labour-intensive. As such, multiple corporations have intensified efforts to...
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Ranking Enhanced Supervised Contrastive Learning for Regression
Supervised contrastive learning has shown promising results in image classification tasks where the representations are pulled together if they share...
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Twin neural network improved k-nearest neighbor regression
Twin neural network regression is trained to predict the difference between the regression targets of two data points rather than the individual...
