The multilabel learning task aims to predict the associated multiple classes of a given
example simultaneously. Such task becomes more challenging when data arrives in stream …

Localizing the root cause of network faults is crucial to network operation and maintenance
(O&M). Significant operational expenses will be saved if the root cause can be identified …

Multi-label classification (MLC) has drawn much attention, thanks to its usefulness and
omnipresence in real-world applications in which objects may be characterized by more …

Lifelong machine learning is emerging in recent years thanks to its ability to use past
knowledge for current problem. Lifelong topic modeling algorithms, such as LTM and AMC …

With the rapid expansion of big data in all fields of science and engineering, multi-label
classification has become a more acute problem in real-world data sets, it is notably difficult …

In this paper we present a comparison between two improved approaches ie hybrid rules-
based method with a proposed wrapper and nearest rule strategy, deep principal …

Recently, deep Convolutional Neural Network (CNN) model has achieved remarkable
results in Natural Language Processing (NLP) tasks, such as information retrieval, relation …

TÓM TẮT Tóm tắt: Học máy suốt đời đang nổi lên trong những năm gần đây nhờ vào khả
năng sử dụng tri thức học được trong quá khứ cho việc học hiện tại, giải quyết được một số …

Multi-label Learning (MLL) is a supervised learning model that hasattracted much attention
of the research community in recent yearsbecause of its wide variety applicability. In this …