I mainly consider the task of learning classifiers from data in this thesis. In this context, I
propose a common framework that combines two different and important paradigms in …

Machine Learning-Modeling Data Locally and Globally presents a novel and unified theory
that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the …

In Machine Learning field, data characteristics usually vary over the space: the overall
distribution might be multi-modal and contain non-linearities. In order to achieve good …

In a supervised setting, the global classification paradigm leverages the whole training data
to produce a single class discriminative model. Alternatively, the local classification …

Traditional global learning, especially generative learning, enjoys a long and distinguished
history, holding a lot of merits, eg a relatively simple optimization, and the flexibility in …

In this chapter, we conduct a more detailed and more formal review on two different schools
of learning approaches, namely, the global learning and local learning. We first provide a …

Machine Learning-Modeling Data Locally and Globally presents a novel and unified theory
that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the …

We develop a novel approach for supervised learning based on adaptively partitioning the
feature space into different regions and learning local region-specific classifiers. We …

In recent years, semi-supervised learning algorithms have aroused considerable interests
from machine learning fields because unlabeled samples are often readily available and …

Abstract Machine learning has achieved tremendous success in a variety of domains in
recent years. However, a lot of these success stories have been in places where the training …