We address a problem of learning ordinal classifiers from partially annotated examples. We introduce a V-shaped interval-insensitive loss function to measure discrepancy between predictions of anâ€¦ (More)

Proceedings of the 21st International Conferenceâ€¦

2012

During the last decade the super-modular Pair-wise Markov Networks (SM-PMN) have become a routinely used model for structured prediction. Their popularity can be attributed to efficient algorithmsâ€¦ (More)

In this paper we study statistical consistency of partial losses suitable for learning structured output predictors from examples containing missing labels. We provide sufficient conditions on dataâ€¦ (More)

We address a problem of learning ordinal classifier from partially annotated examples. We introduce an interval-insensitive loss function to measure discrepancy between predictions of an ordinalâ€¦ (More)

A number of algorithms and its applications for automatic classifiers learning from examples is ever growing. Most of existing algorithms require a training set of completely annotated examples,â€¦ (More)

We present results of a multi-site photometric campaign on the high-amplitude Î´ Scuti star KIC 6382916 in the Kepler field. The star was observed over a 85-d interval at five different sites in Northâ€¦ (More)

We show that classification rules used in ordinal regression are equivalent to a certain class of linear multi-class classifiers. This observation not only allows to design new learning algorithmsâ€¦ (More)