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IEEE Regression Algorithms Projects at MTech Projects feature the latest project topics with Abstract, Source Code, Thesis, Report, Documentation, Research Paper Writing, Presentation ppt, Video demo, and Expert guidance for BTech, MTech, BE, ME, MS and PhD students. We support engineering students and researchers across Hyderabad, Bengaluru, Chennai, Mumbai, Delhi, Pune, Kolkata, Ahmedabad, Kochi and other major cities in India, USA, UK.

Latest Regression Algorithms IEEE Project Titles [2026]

S.No Project Title Abstract
1 Generalized Kernel Methods for Scaling Abstract
2 Multiclass Kernel Models Using Robust Variational Learning and Stein Refinement Abstract
3 Event Recommendation Preference and Constraint Factor Model Abstract
4 Acceleration of Nonsmooth Convex Optimization with Constraints Individual Convergence Abstract
5 Using structural MRI, a multi-task weakly supervised attention network can estimate a person's level of dementia. Abstract
6 Extreme Learning Machines Using Mixture Correntropy Abstract
7 PPD: A Parallel Primal-Dual Coordinate Descent Algorithm that is Scalable and Effective Abstract
8 Head Pose Estimation Using Multivariate Label Distribution Abstract
9 Learning the global negative correlation A Unified Framework for Global Ensemble Model Optimization Abstract
10 Personalized and context-aware multi-modal transportation recommendation using data from multiple urban sources Abstract
11 Generalized Metric Learning for Factorization Machines Enhancement Abstract
12 Affine Matrix Rank Minimization on a Large Scale Using a Novel Nonconvex Regularizer Abstract
13 Classification of Imbalanced Images Using Deep Attention Abstract
14 Efficient Kernel Aggregation Query Algorithms Abstract
15 Deep Cross-Output Knowledge Transfer Using Support Vector Machines with Stacked-Structure Least Squares Abstract
16 CuWide: Towards Efficient Flow-based Sparse Wide Models Training on GPUs Abstract
17 Consensus Accelerated Proximal Reweighted Iteration for a Class of Nonconvex Minimizations (Capri) Abstract
18 A Hybrid System for Time Series Forecasting Based on Dynamic Selection Abstract

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