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.

Ex Input:
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate O( 1 ε2 ) improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate O( 1 ε4 ).

Ex Output:
Conditional Accelerated Lazy Stochastic Gradient Descent


Ex Input:
The downfall of many supervised learning algorithms, such as neural networks, is the inherent need for a large amount of training data (Benediktsson et al., 1993). Although there is a lot of buzz about big data, there is still the problem of doing classification from a small data-set. Other methods such as support vector machines, although capable of dealing with few samples, are inherently binary classifiers (Cortes and Vapnik, 1995), and are in need of learning strategies such as One vs All in the case of multi-classification. In the presence of a large number of classes this can become problematic. In this paper we present, a novel approach to supervised learning through the method of clustering. Unlike traditional methods such as K-Means (MacQueen, 1967), Gravitational Clustering does not require the initial number of clusters, and automatically builds the clusters, individual samples can be arbitrarily weighted and it requires only few samples while staying resilient to over-fitting. Keywords—Machine Learning, Classification, Clustering.

Ex Output:
Introduction to Gravitational Clustering


Ex Input:
Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.

Ex Output:

MusicMood: Predicting the mood of music from song lyrics using machine learning