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.

Example Input: This paper presents capabilities of using genetic algorithms to find approximations of function extrema, which cannot be found using analytic ways. To enhance effectiveness of calculations, algorithm has been parallelized using OpenMP library. We gained much increase in speed on platforms using multithreaded processors with shared memory free access. During analysis we used different modifications of genetic operator, using them we obtained varied evolution process of potential solutions. Results allow to choose best methods among many applied in genetic algorithms and observation of acceleration on Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores.
Example Output: GENERATING EXTREMA APPROXIMATION OF ANALYTICALLY INCOMPUTABLE FUNCTIONS THROUGH USAGE OF PARALLEL COMPUTER AIDED GENETIC ALGORITHMS

Example Input: 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.
Example Output: Multilabel Prediction via Calibration

Example Input: Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are subcategories of single-label classification. Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification. In this paper, a novel online universal classifier capable of performing all the three types of classification is proposed. Being a high speed online classifier, the proposed technique can be applied to streaming data applications. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types. KeywordsUniversal, Classification, Binary, Multi-class, Multi-label, Online, Extreme learning machines, Data stream.
Example Output:
An Online Universal Classifier for Binary, Multi- class and Multi-label Classification