Its in-built functions, like glm and lm, allow professionals to engage in statistical modeling. The Caret package makes it possible to build, train, and test machine learning models. Caret also allows hyperparameter tuning, bringing optimization to the performance of their ML algorithms. ...
machine-learningoptimizationjuliaadmmoptimal-controlproximal-algorithmsproximal-operatorsnumerical-optimizationoptimization-algorithmsnonlinear-programmingnonsmooth-optimizationproximal-gradient-method UpdatedJan 5, 2025 Julia Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous...
Computer science - Algorithms, Complexity, Programming: An algorithm is a specific procedure for solving a well-defined computational problem. The development and analysis of algorithms is fundamental to all aspects of computer science: artificial intell
Learn machine learning algorithms including topics like Linear regression, Logistic regression and more advanced topics such as decision tress, random forests and support vector machines Variety of R programming exercises, capstone projects and Machine Learning portfolio projects Access to online Q&A forum ...
The proposed model had a start- ing point in two known bounded number of processors algorithms: Modified Critical Path and Highest Level First With Estimated Times. Regarding the implementation, a simulator was used to analyze and design the scheduling algorithms. Finally, in [7] an efficient ...
The Synthetic Minority Over-sampling Technique (SMOTE) was created to address class-imbalance problems in machine learning algorithms. The idea is to oversample from the rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et ...
There are several approaches to categorize Application Partitioning Algorithms (APAs). Khan et al.[19]proposed a taxonomy of APAs based on their strategies to break outapplication executioncomponents into non-offloadable and offloadable modules. A partitioning algorithm could be classified into either ...
linear programming, mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. This technique has been useful for guiding quantitative decisions in business planning, in industrial engineering, and—to a lesser extent—in the social and phy...
Considering multiple sources of uncertainty however might help decision makers find improved intermodal routes [1]. As a result, in this study, we will consider both road travel time uncertainty and loading/unloading time uncertainty when modeling the road-rail intermodal routing, so that road-rail...
The works referred in this section suggest that the incorporation of fuzzy modeling could lead to positive results in collaborative filtering-based, e-learning recommendation. The proposal developed in the current paper, aims at using fuzzy tools to improve recommendations in POJs scenario. 2.3. Reco...