Supervised machine learning : optimization framework and applications with SAS and R / Tanya Kolosova and Samuel Berestizhevsky.
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- text
- computer
- 9780429297595
- 0429297599
- 1000176835
- Supervised learning (Machine learning)
- Program transformation (Computer programming)
- SAS (Computer program language)
- R (Computer program language)
- Apprentissage supervisé (Intelligence artificielle)
- Transformation de programme (Informatique)
- SAS (Langage de programmation)
- R (Langage de programmation)
- COMPUTERS -- Machine Theory
- COMPUTERS -- Mathematical & Statistical Software
- Program transformation (Computer programming)
- Supervised learning (Machine learning)
- 006.31 23
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds |
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Junaid Zaidi Library, COMSATS University Islamabad Ground Floor | Books | 006.31 KOL-S 63322 (Browse shelf(Opens below)) | Available | Hardback | 10001000063322 |
Includes bibliographical references.
IntroductionPART 1Introduction to the AI frameworkSupervised Machine Learning and Its Deployment in SAS and RBootstrap methods and Its Deployment in SAS and ROutliers Detection and Its Deployment in SAS and RDesign of Experiment and Its Deployment in SAS and RPART IIIntroduction to the SAS and R based table-driven environmentInput Data componentDesign of Experiment for Machine-Learning component"Contaminated" Training Datasets ComponentPART IIIInsurance Industry: Underwriters decision-making processInsurance Industry: Claims Modeling and PredictionIndex
AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub
Tanya Kolosova is a statistician, software engineer, an educator, and a co-author of two books on statistical analysis and metadata-based applications development using SAS. Tanya is an actionable analytics expert, she has extensive knowledge of software development methods and technologies, artificial intelligence methods and algorithms, and statistically designed experiments. Samuel Berestizhevsky is a statistician, researcher and software engineer. Together with Tanya, Samuel co-authored two books on statistical analysis and metadata-based applications development using SAS. Samuel is an innovator and an expert in the area of automated actionable analytics and artificial intelligence solutions. His extensive knowledge of software development methods, technologies and algorithms allows him to develop solutions on the cutting edge of science.
Online resource; title from PDF title page (Taylor & Francis, viewed November 16, 2020).
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