The best thesis I found in this field is "Efficient Boosted Ensemble-Based Machine Learning In The Context Of Cascaded Frameworks", written by Teo Susnjak. It is a very useful resource and also a new one, but I need more resources.
See our recent paper on ensemble learning:
Conference Paper On performance of Meta-learning Templates on Different Datasets
There is the nice paper by Graf,Vapnik et al., 2004, "Parallel Support Vector Machines:
The Cascade SVM". It is also worth to look at other ensemble techniques (bagging, boosting), see e.g. the paper Valentini, 2004, "Random aggregated and bagged ensembles of SVMs: an empirical bias–variance analysis" or the paper of Wang et al., 2009, "Empirical analysis of support vector machine ensemble classifiers". I'm attaching the Graf-publication, the other are easily found on the Internet
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