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Multi-task optimization is a paradigm in the optimization literature that focuses on solving multiple self-contained tasks simultaneously. [ 1 ] [ 2 ] The paradigm has been inspired by the well-established concepts of transfer learning [ 3 ] and multi-task learning [ 4 ] in predictive analytics .
Salesforce management systems (also sales force automation systems (SFA)) are information systems used in customer relationship management (CRM) marketing and management that help automate some sales and sales force management functions. They are often combined with a marketing information system, in which case they are often called CRM systems
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Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.
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Laboratory-based studies of multi-tasking indicate that one motivation for switching between tasks is to increase the time spent on the task that produces the most reward (Payne, Duggan & Neth, 2007). This reward could be progress towards an overall task goal, or it could simply be the opportunity to pursue a more interesting or fun activity.
MTC is reminiscent of HTC, but it "differs in the emphasis of using many computing resources over short periods of time to accomplish many computational tasks (i.e. including both dependent and independent tasks), where the primary metrics are measured in seconds (e.g. FLOPS, tasks/s, MB/s I/O rates), as opposed to operations (e.g. jobs) per month.
Cooperative multitasking, also known as non-preemptive multitasking, is a style of computer multitasking in which the operating system never initiates a context switch from a running process to another process.