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: Finding inclusion-minimal hitting sets for a given collection of sets is a fundamental combinatorial problem with applications in domains as diverse as Boolean algebra, computational biology, and data mining. Much of the algorithmic literature focuses on the problem of recognizing the collection of minimal hitting sets; however, in many of the applications, it is more important to generate these hitting sets. We survey twenty algorithms from across a variety of domains, considering their history, classification, useful features, and computational performance on a variety of synthetic and real-world inputs. We also provide a suite of implementations of these algorithms with a ready-to-use, platform-agnostic interface based on Docker containers and the AlgoRun framework, so that interested computational scientists can easily perform similar tests with inputs from their own research areas on their own computers or through a convenient Web interface.

A: The minimal hitting set generation problem: algorithms and computation
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Q: This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWSs (e.g., Boolean and term-frequency schemes), the definition of TWSs has been traditionally an art. Further, it is still a difficult task to determine what is the best TWS for a particular problem and it is not clear yet, whether better schemes, than those currently available, can be generated by combining known TWS. We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification. The genetic program learns how to combine a set of basic units to give rise to discriminative TWSs. We report an extensive experimental study comprising data sets from thematic and non-thematic text classification as well as from image classification. Our study shows the validity of the proposed method; in fact, we show that TWSs learned with the genetic program outperform traditional schemes and other Corresponding author. Email addresses: hugojair@inaoep.mx (Hugo Jair Escalante), mauricio.garcia.cs@gmail.com (Mauricio A. Garćıa-Limón), a.morales@inaoep.mx (Alicia Morales-Reyes), mgraffg@gmail.com (Mario Graff), mmontesg@inaoep.mx (Manuel Montes-y-Gómez), emorales@inaoep.mx (Eduardo F. Morales) Preprint submitted to Elsevier October 8, 2014 TWSs proposed in recent works. Further, we show that TWSs learned from a specific domain can be effectively used for other tasks.

A: Term-Weighting Learning via Genetic Programming for Text Classification
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Q: One of the benefits of belief networks and influence diagrams is that so much knowl­ edge is captured in the graphical structure. In particular, statements of conditional irrel­ evance (or independence) can be verified in time linear in the size of the graph. To re­ solve a particular inference query or decision problem, only some of the possible states and probability distributions must be specified, the "requisite information." This paper presents a new, simple, and effi­ cient "Bayes-ball" algorithm which is well­ suited to both new students of belief net­ works and state of the art implementations. The Bayes-ball algorithm determines irrele­ vant sets and requisite information more ef­ ficiently than existing methods, and is linear in the size of the graph for belief networks and influence diagrams.

A:
Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
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