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 propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Example output: A Neural Local Coherence Model
Example explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Q: A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. One approach to concept drift handling is by feeding the whole training data set once again into a learning machine for retraining. Another approach is by rebuilding an ensemble classifiers to adapt to a new training data set. In either approach, retraining or rebuilding classifiers are expensive and not practical. In this paper, we propose an enhancement of Online-Sequential Extreme Learning Machine (OS-ELM) and its variant Constructive Enhancement OS-ELM (CEOS-ELM) by adding an adaptive capability for classification and regression problem. The scheme is named as Adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and both drifts occurred at the same time (hybrid drift). The AOS-ELM also works well for sudden drift as well as recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using various public dataset widely used for concept drift verification from SEA and STAGGER; and other public datasets such as MNIST and USPS. Experiments show that our method gives higher kappa value compared to the multi-classifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose to take the rank of the pseudo inverse matrix as an indicator parameter to detect 'under-fitting' condition. Keywords adaptive, concept drift, extreme learning machine, online sequential.
A:
Adaptive Online Sequential ELM for Concept Drift Tackling