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: We present a concrete design for Solomonoff’s incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on a stochastic Context Free Grammar together with new update algorithms that use the same grammar as a guiding probability distribution for incremental machine learning. The updates include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. The issues of extending the a priori probability distribution and bootstrapping are discussed. We have implemented a good portion of the proposed algorithms. Experiments with toy problems show that the update algorithms work as expected.
Example Output: Gigamachine: incremental machine learning on desktop computers

Example Input: This paper studies theoretically and empirically a method of turning machinelearning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise for large data sets) probabilities. When these imprecise probabilities are merged into precise probabilities, the resulting predictors, while losing the theoretical property of perfect calibration, are consistently more accurate than the existing methods in empirical studies. The conference version of this paper is to appear in Advances in Neural Information Processing Systems 28, 2015.
Example Output: Large-scale probabilistic prediction with and without validity guarantees

Example Input: Stemming or suffix stripping, an important part of the modern Information Retrieval systems, is to find the root word (stem) out of a given cluster of words. Existing algorithms targeting this problem have been developed in a haphazard manner. In this work, we model this problem as an optimization problem. An Integer Program is being developed to overcome the shortcomings of the existing approaches. The sample results of the proposed method are also being compared with an established technique in the field for English language. An AMPL code for the same IP has also been given.
Example Output:
Suffix Stripping Problem as an Optimization Problem