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: A two–step Christoffel function based solution is proposed to distribution regression problem. On the first step, to model distribution of observations inside a bag, build Christoffel function for each bag of observations. Then, on the second step, build outcome variable Christoffel function, but use the bag’s Christoffel function value at given point as the weight for the bag’s outcome. The approach allows the result to be obtained in closed form and then to be evaluated numerically. While most of existing approaches minimize some kind an error between outcome and prediction, the proposed approach is conceptually different, because it uses Christoffel function for knowledge representation, what is conceptually equivalent working with probabilities only. To receive possible outcomes and their probabilities Gauss quadrature for second–step measure can be built, then the nodes give possible outcomes and normalized weights – outcome probabilities. A library providing numerically stable polynomial basis for these calculations is available, what make the proposed approach practical.
Example Output: Multiple–Instance Learning: Christoffel Function Approach to Distribution Regression Problem

Example Input: Clustering is a useful data exploratory method with its wide applicability in multiple fields. However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions. This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization. The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers. Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively. An extensive analysis highlights the superior performance of the proposed method over other techniques.
Example Output: Variance Based Moving K-Means Algorithm

Example Input: For any deep computational processing of language we need evidences, and one such set of evidences is corpus. This paper describes the development of a textbased corpus for the Bishnupriya Manipuri language. A Corpus is considered as a building block for any language processing tasks. Due to the lack of awareness like other Indian languages, it is also studied less frequently. As a result the language still lacks a good corpus and basic language processing tools. As per our knowledge this is the first effort to develop a corpus for Bishnupriya Manipuri language. KeywordsCorpus, language, Bishnupriya Manipuri, word analysis, word frequency
Example Output:
Towards The Development of a Bishnupriya Manipuri Corpus