In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: We propose an efficient technique for multilabel classification based on calibration, a term we use to mean learning a link function that maps independent predictions to joint predictions. Though a naive implementation of our proposal would require training individual classifiers for each label, we show that for natural datasets and linear classifiers we can sidestep this by leveraging techniques from randomized linear algebra. Moreover, our algorithm applies equally well to multiclass classification. The end result is an algorithm that scales to very large multilabel and multiclass problems, and offers state of the art accuracy on many datasets.

A: Multilabel Prediction via Calibration
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Q: *Correspondence: aljadda@uga.edu 1Department of Computer Science, University of Georgia, Athens,GA, USA Full list of author information is available at the end of the article †Equal contributor Abstract Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network, which is one of the most common PGMs used in machine learning and data mining, demonstrates this limitation when the training data consists of random variables, each of them has a large set of possible values. In the big data era, one would expect new extensions to the existing PGMs to handle the massive amount of data produced these days by computers, sensors and other electronic devices. With hierarchical data data that is arranged in a treelike structure with several levels one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian Networks become infeasible for representing the probability distributions. In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space. The proposed model achieves perfect precision of 1.0 and high recall of 0.93 when it is used as multi-label classifier for the annotation of mass spectrometry data. On another data set of 1.5 billion search logs provided by CareerBuilder.com the model was able to predict latent semantic relationships between search keywords with accuracy up to 0.80.

A: Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
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Q: Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HOLE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HOLE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-ofthe-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.

A:
Holographic Embeddings of Knowledge Graphs
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