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A literature review machine learning

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Basto and S. Abstract The amount of data extracted from production processes has increased exponentially due to the proliferation of sensing technologies. When processed and analyzed, data can bring out valuable information and knowledge from manufacturing process, production system and equipment.


Machine Learning in Agriculture: A Review

Start reading List of all articles. The General Insurance Machine Learning in Reserving working party is an international group of over 40 actuaries, bringing together experts in this field from around the globe. The idea of the working party is to help move this forward, by identifying what the barriers are, communicating any benefits, and helping develop the research techniques in pragmatic ways. At the same time we understand the resource and time pressures that reserving actuaries are under and the aim is not to replace existing reserving methods per se, but to start the journey to understanding if and how machine learning may help us in our day to day work. Our intention is to develop and undertake our own research.

Machine Learning in Agriculture: A Review

The literature review workstream collects published research on Machine Learning ML in reserving techniques. By summer , the workstream reviewed a total of 69 papers and continues to scan for new relevant papers to enrich the lessons learned. There is a growing body of literature on application of ML techniques to general insurance reserving. The majority use individual claims data, and this possibly could be extended to cover micro-level data points in the region surrounding the claim event to include: transaction dates, reopened claims, closing dates etc.
The improvements made in the last couple of decades in the requirements engineering RE processes and methods have witnessed a rapid rise in effectively using diverse machine learning ML techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow SO for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing NLP techniques.

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