By Balaji Krishnapuram,Shipeng Yu,R. Bharat Rao
In desktop studying purposes, practitioners needs to take note of the cost associated with the set of rules. those bills contain:
- Cost of buying education data
- Cost of knowledge annotation/labeling and cleaning
- Computational expense for version becoming, validation, and testing
- Cost of amassing features/attributes for attempt data
- Cost of consumer suggestions collection
- Cost of unsuitable prediction/classification
Cost-Sensitive desktop Learning is among the first books to supply an summary of the present examine efforts and difficulties during this zone. It discusses real-world functions that contain the price of studying into the modeling method.
The first a part of the ebook offers the theoretical underpinnings of cost-sensitive computing device studying. It describes well-established computing device studying techniques for decreasing info acquisition bills in the course of education in addition to methods for lowering charges whilst platforms needs to make predictions for brand new samples. the second one half covers real-world functions that successfully alternate off forms of bills. those purposes not just use conventional computer studying techniques, yet additionally they include state-of-the-art learn that advances past the constraining assumptions by way of reading the applying wishes from first principles.
Spurring extra study on a number of open difficulties, this quantity highlights the customarily implicit assumptions in computing device studying innovations that weren't totally understood some time past. The e-book additionally illustrates the industrial significance of cost-sensitive laptop studying via its assurance of the fast software advancements made by means of best businesses and educational examine labs.
Read or Download Cost-Sensitive Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition) PDF
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Extra info for Cost-Sensitive Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Cost-Sensitive Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition) by Balaji Krishnapuram,Shipeng Yu,R. Bharat Rao
Categories: Machine Theory