Regarding the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5), it is required to classify label sets of speech acts on human-to-human dialogues. In this paper, we propos...
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ISBN:
(纸本)9781509030156
Regarding the spoken language understanding (SLU) pilot task of the Dialog State Tracking Challenge 5 (DSTC5), it is required to classify label sets of speech acts on human-to-human dialogues. In this paper, we propose a multi-label classification model with the assistance of algorithm adaptation method. To be specific, a Convolutional Neural Network (CNN) model on top of pre-trained word vectors is adapted for the multi-label classification task by utilizing a threshold learning mechanism. In order to evaluate the performance of our proposed model, comparative experiments on the DSTC5 dialogue datasets are conducted. Experimental results show that the proposed model outperforms most of the submitted model in the DSTC5 in terms of F1-score. Without any manually designed features, our model has advantage of handling the multi-label SLU task, using only publicly available pre-trained word vectors.
Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel cla...
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ISBN:
(纸本)9781467381437
Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel classification for Indonesian news articles. Since this classification deals with a large number of features, we also employ some feature selection methods to reduce feature dimension. There are four factors as the focuses of this paper, i. e., feature weighting method, feature selection method, multilabel classification approach, and singlelabel classification algorithm. These factors will be combined to determine the best combination. The experiments show that the best performer for multilabel classification of Indonesian news articles is the combination of TF-IDF feature weighting method, Symmetrical Uncertainty feature selection method, Calibrated Label Ranking -which belongs to problem transformation approach-, and SVM algorithm. This best combination achieves F-measure of 85.13% in 10-fold cross-validation, but the Fmeasure decreases to 76.73% in testing because of OOV.
EULAG (Eulerian/semi-Lagrangian fluid solver) is an established numerical model for simulating thermo-fluid flows across a wide range of scales and physical scenarios. The multidimensional positive definite advection ...
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ISBN:
(纸本)9783319219097;9783319219080
EULAG (Eulerian/semi-Lagrangian fluid solver) is an established numerical model for simulating thermo-fluid flows across a wide range of scales and physical scenarios. The multidimensional positive definite advection transport algorithm (MPDATA) is among the most time-consuming components of EULAG. In this study, we focus on adapting the 3D MPDATA computations to clusters with graphics processors. Our approach is based on a hierarchical decomposition including the level of cluster, as well as an optimized distribution of computations between GPU resources within each node. To implement the resulting computing scheme, the MPI standard is used across nodes, while CUDA is applied inside nodes. We present performance results for the 3D MPDATA code running on the NVIDIA GeForce GTX TITAN graphics card, as well as on the Piz Daint cluster equipped with NVIDIA Tesla K20x GPUs. In particular, the sustained performance of 138 Gflop/s is achieved for a single GPU, which scales up to more than 11 Tflop/s for 256 GPUs.
作者：
Zhang, Min-LingZhou, Zhi-HuaSoutheast Univ
Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China Southeast Univ
Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 210096 Jiangsu Peoples R China Nanjing Univ
Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been ...
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Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.
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