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: While known algorithms for sensitivity analysis and parameter tuning in probabilistic networks have a running time that is exponential in the size of the network, the exact computational complexity of these problems has not been established as yet. In this paper we study several variants of the tuning problem and show that these problems are NP-complete in general. We further show that the problems remain NP-complete or PP-complete, for a number of restricted variants. These complexity results provide insight in whether or not recent achievements in sensitivity analysis and tuning can be extended to more general, practicable methods.
Example Output: The Computational Complexity of Sensitivity Analysis and Parameter Tuning

Example Input: In this research we address the problem of capturing recurring concepts in a data stream environment. Recurrence capture enables the re-use of previously learned classifiers without the need for re-learning while providing for better accuracy during the concept recurrence interval. We capture concepts by applying the Discrete Fourier Transform (DFT) to Decision Tree classifiers to obtain highly compressed versions of the trees at concept drift points in the stream and store such trees in a repository for future use. Our empirical results on real world and synthetic data exhibiting varying degrees of recurrence show that the Fourier compressed trees are more robust to noise and are able to capture recurring concepts with higher precision than a meta learning approach that chooses to re-use classifiers in their originally occurring form.
Example Output: Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform

Example Input: We address the problem of general supervised learning when data can only be ac-<lb>cessed through an (indefinite) similarity function between data points. Existing<lb>work on learning with indefinite kernels has concentrated solely on binary/multi-<lb>class classification problems. We propose a model that is generic enough to handle<lb>any supervised learning task and also subsumes the model previously proposed for<lb>classification. We give a “goodness” criterion for similarity functions w.r.t. a given<lb>supervised learning task and then adapt a well-known landmarking technique to<lb>provide efficient algorithms for supervised learning using “good” similarity func-<lb>tions. We demonstrate the effectiveness of our model on three important super-<lb>vised learning problems: a) real-valued regression, b) ordinal regression and c)<lb>ranking where we show that our method guarantees bounded generalization error.<lb>Furthermore, for the case of real-valued regression, we give a natural goodness<lb>definition that, when used in conjunction with a recent result in sparse vector re-<lb>covery, guarantees a sparse predictor with bounded generalization error. Finally,<lb>we report results of our learning algorithms on regression and ordinal regression<lb>tasks using non-PSD similarity functions and demonstrate the effectiveness of<lb>our algorithms, especially that of the sparse landmark selection algorithm that<lb>achieves significantly higher accuracies than the baseline methods while offering<lb>reduced computational costs.
Example Output:
Supervised Learning with Similarity Functions