文件名称：机器学习 经典文章 入门必备
文件大小：3.22MB
文件格式：RAR
更新时间：2022-06-08 13:23:34
machine learning ai kernel learning
人工智能 机器学习 入门必备的经典文章 包含Learnability, stability and uniform convergence，Learning_Theory_Estimates_via_Integral_Operators_and_Their_Approximations，Local Rademacher complexities，On regularization algorithms in learning theory，On the mathematical foundations of learning等十余篇经典文章，入门首选
【文件预览】：
经典文章
----Optimal Rates for Regularized Least Squares Regression.pdf(244KB)
----Rademacher and Gaussian complexities Risk bounds and structural results.pdf(279KB)
----Regularization in kernel learning.pdf(393KB)
----Statistical performance of support vector machines.pdf(456KB)
----Learning_Theory_Estimates_via_Integral_Operators_and_Their_Approximations.pdf(403KB)
----Local Rademacher complexities.pdf(268KB)
----Optimal rates for the regularized least-squares algorithm.pdf(432KB)
----Stability and Generalization.pdf(301KB)
----On regularization algorithms in learning theory.pdf(243KB)
----On the mathematical foundations of learning.pdf(568KB)
----Learnability, stability and uniform convergence.pdf(323KB)