论文信息 - Introduction to Learning - 字舞流文

Introduction to Learning

This chapter presents the basic definitions and notation related to the problem of learning from data samples. It shows how a learner (e.g., classifier) is built, and its performance is evaluated using multiple real and academic examples.

Moamar Sayed-Mouchaweh

[1] Padraig Cunningham,et al. A case-based technique for tracking concept drift in spam filtering , 2004, Knowl. Based Syst..

[2] M. Pardo,et al. Classification of electronic nose data with support vector machines , 2005 .

[3] Shankar Vembu,et al. Chemical gas sensor drift compensation using classifier ensembles , 2012 .

[4] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[5] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.

[6] David G. Stork,et al. Pattern Classification , 1973 .

[7] Ke Meng,et al. Self-adaptive radial basis function neural network for short-term electricity price forecasting , 2009 .

[8] Walmir M. Caminhas,et al. A review of machine learning approaches to Spam filtering , 2009, Expert Syst. Appl..

[9] Salvatore J. Stolfo,et al. Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection , 1998, KDD.