Algorithmic Learning in a Random World
- المؤلف
- Vladimir Vovk, Alexander Gammerman, Glenn Shafer
- سنة النشر
- 2005
- الناشر
- Springer
- لغة الملف
- انكليزي
- نوع الملف
- Book
- تصنيف الكتاب
- Computer Science
- تحميل الكتاب قراءة الكتاب
This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap.
الكلمات المفتاحية: Computer science / Approximation / Conformal prediction / Randomness / Regression / Algorithms / Classification / Learning / Machine learning / Modeling
