One-Pass Reductions

One-Pass Boosting

This paper studies boosting algorithms that make a single pass over a set of base classifiers. We first analyze a one-pass algorithm in the setting of boosting with diverse base classifiers. Our guarantee is the same as the best proved for any boosting algorithm, but our one-pass algorithm is much faster than previous approaches. We next exhibit a random source of examples for which a “picky” v...

One-Pass AUC Optimization

AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set. In this work, we focus on one-pass AUC optimization that requires going through the training data only once without storing the entire training dataset, where conventional online learning algorithms cannot be applied directly bec...

Distribution-Free One-Pass Learning

In many large-scale machine learning applications, data are accumulated with time, and thus, an appropriate model should be able to update in an online paradigm. Moreover, as the whole data volume is unknown when constructing the model, it is desired to scan each data item only once with a storage independent with the data volume. It is also noteworthy that the distribution underlying may chang...

One-Pass Multi-View Learning

Multi-view learning has been an important learning paradigm where data come from multiple channels or appear in multiple modalities. Many approaches have been developed in this field, and have achieved better performance than single-view ones. Those approaches, however, always work on small-size datasets with low dimensionality, owing to their high computational cost. In recent years, it has be...

One - Pass Evaluation of Region