Supervised Machine Learning: Optimization Framework and Applications with SAS and R (Hardcover)
暫譯: 監督式機器學習：使用 SAS 和 R 的優化框架與應用 (精裝本)

Kolosova, Tanya, Berestizhevsky, Samuel

Supervised Machine Learning: Optimization Framework and Applications with SAS and R (Hardcover)

商品描述

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

商品描述(中文翻譯)

AI 框架旨在解決實際應用中監督學習方法的偏差-方差權衡問題。該 AI 框架包括自助法（bootstrapping），以創建具有不同特徵的多個訓練和測試數據集，設計和分析統計實驗以識別最佳特徵子集和最佳超參數（hyper-parameters）以用於機器學習（ML）方法，以及數據污染以測試分類器的穩健性。

**主要特點：**

- 單獨使用機器學習方法並不保證能構建對新數據具有良好泛化能力的分類器
- 通過設計和分析統計實驗可以解決機器學習方法的最佳特徵子集和超參數的識別
- 使用自助法進行大量的訓練和測試數據集的抽樣，具有不同的數據特徵（例如：受污染的訓練集），可以處理偏差問題
- 開發基於 SAS 的表驅動環境，允許管理與所提議的 AI 框架相關的所有元數據，並與 R 庫實現互操作性，以完成各種統計和機器學習任務
- 創建 AI 框架的 R 和 SAS 程式碼可在 GitHub 上獲得

作者簡介

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.

作者簡介(中文翻譯)

Tanya Kolosova 是一位統計學家、軟體工程師、教育工作者，並且是兩本關於使用 SAS 進行統計分析和基於元數據的應用程式開發的書籍的共同作者。Tanya 是一位可行性分析專家，擁有豐富的軟體開發方法和技術、人工智慧方法和演算法，以及統計設計實驗的知識。

Samuel Berestizhevsky 是一位統計學家、研究員和軟體工程師。Samuel 與 Tanya 共同撰寫了兩本關於使用 SAS 進行統計分析和基於元數據的應用程式開發的書籍。Samuel 是一位創新者，也是自動化可行性分析和人工智慧解決方案領域的專家。他對軟體開發方法、技術和演算法的廣泛知識使他能夠開發出位於科學前沿的解決方案。

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