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Applying machine learning to market analysis:Knowing your luxury consumer

Journal of Management Analytics 2019年第4期6卷 404-419页

作者： Kuo Chi-Hsien Shinya Nagasawa Department of Commerce Waseda UniversityShinjuku-kuJapan

Chinese consumer research in the luxury sector is the emphasis in the business research ***,it can be cost-intensive or time-consuming to interpret big data from any research conducted in the *** this paper,the researchers created a machine-learning model to help minimize those research *** study analyzed Chinese luxury consumption behavior,while the Chinese contributed 33%of the global luxury market in 2018 and play as a growth engine in the luxury market(Bain&***://***/insights/whats-powering-chinas-market-for-luxury-goods/).The researchers interpreted this analysis using machine-learning algorithms through different sets of conditions and then proposed an understandable and highly accurate machine-learning *** traditional statistical methods,which rely on domain experts to create handcrafted features,this paper proposes an unsupervised end-to-end model that can directly and accurately process questionnaire data without human *** paper also demonstrates how to practically apply an automatic unsupervised analysis method(PCA)to find inferences in the big data,and helps interpret the implied meaning to the questions.

关键词： Luxury consumer machine learning end-to-end model unsupervised learning principal component analysis(PCA)

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Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and end-To-end Mode

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Journal on Internet of Things 2019年第1期1卷 17-23页

作者： Yue Zhao Jianjian Yue Wei Song Xiaona Xu Xiali Li Licheng Wu Qiang Ji School of Information and Engineering Minzu University of ChinaBeijing100081China Rensselaer Polytechnic Institute 110 Eighth StreetTroy NY 12180-3590USA

We proposed a method using latent regression Bayesian network (LRBN) toextract the shared speech feature for the input of end-to-end speech recognition *** structure of LRBN is compact and its parameter learning is fast. Compared withConvolutional Neural Network, it has a simpler and understood structure and lessparameters to learn. Experimental results show that the advantage of hybridLRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classificationarchitecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN ishelpful to differentiate among multiple language speech sets.

关键词： Multi-dialect speech recognition Tibetan language latent regressionbayesian network end-to-end model

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An end-to-end Method for Joint Extraction of Tibetan Entity Relations

An End-to-End Method for Joint Extraction of Tibetan Entity ...

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作者： Yuan Sun Sisi Liu Tianci Xia Xiaobing Zhao School of Information Engineering Minzu University of China Minority Languages Branch National Language Resource and Monitoring Research Center

Entity relation extraction is to find entities and relations from unstructured texts, which is beneficial to the applications of knowledge graphs and question answering systems. The traditional methods handle this task in a pipelined manner which extracts the entities first and then recognizes their relations. This framework may lead to error delivery. In order to tackle this problem, this paper proposes an end-to-end method for joint extraction of Tibetan entity relations which can extract entities and relations at the same time. According to the Tibetan spelling characteristics, this paper processes the Tibetan corpus by word-level and character-level respectively. Combined with part of speech tagging, we use the end-to-end model to convert the entity relation extraction task to the tagging problem. Finally, the experimental results show that the proposed method is better than the baseline.

关键词： end-to-end model Tibetan Entity Relation Joint Method Character-Level Processing

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