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.
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Question: We present a neural network architecture to predict a point in color space from the sequence of characters in the color’s name. Using large scale color–name pairs obtained from an online color design forum, we evaluate our model on a “color Turing test” and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at http://colorlab.us.

Answer: Character Sequence Models for Colorful Words


Question: 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.

Answer: Mining Recurrent Concepts in Data Streams using the Discrete Fourier Transform


Question: In this paper the behavior of various be­ lief network learning algorithms is stud­ ied. Selecting belief networks with cer­ tain minimallity properties turns out to be NP-hard, which justifies the use of search heuristics. Search heuristics based on the Bayesian measure of Cooper and Her­ skovits and a minimum description length (MDL) measure are compared with re­ spect to their properties for both limit­ ing and finite database sizes. It is shown that the MDL measure has more desir­ able properties than the Bayesian mea­ sure. Experimental results suggest that for learning probabilities of belief net­ works smoothing is helpful.

Answer:
Properties of Bayesian Belief Network Learning Algorithms