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1. 发明申请

WO2016165120A1 DEEP NEURAL SUPPORT VECTOR MACHINES 审中-公开
标题翻译：深层神经支持矢量机
公开(公告)号：WO2016165120A1
公开(公告)日：2016-10-20
申请号：PCT/CN2015/076857
申请日：2015-04-17
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC , ZHANG, Shixiong , LIU, Chaojun , YAO, Kaisheng , GONG, Yifan
发明人： ZHANG, Shixiong , LIU, Chaojun , YAO, Kaisheng , GONG, Yifan
IPC分类号： G10L15/02
CPC分类号： G10L15/16 , G06N3/02 , G06N99/005 , G10L15/187 , G10L2015/025
摘要： Aspects of the technology described herein relates to a new type of deep neural network (DNN). The new DNN is described herein as a deep neural support vector machine (DNSVM). Traditional DNNs use the multinomial logistic regression (softmax activation) at the top layer and underlying layers for training. The new DNN instead uses a support vector machine (SVM) as one or more layers, including the top layer. The technology described herein can use one of two training algorithms to train the DNSVM to learn parameters of SVM and DNN in the maximum-margin criteria. The first training method is a frame-level training. In the frame-level training, the new model is shown to be related to the multiclass SVM with DNN features. The second training method is the sequence-level training. The sequence-level training is related to the structured SVM with DNN features and HMM state transition features.
摘要翻译：本文描述的技术的方面涉及一种新型的深神经网络（DNN）。新DNN在本文中被描述为深神经支持向量机（DNSVM）。传统的DNN使用顶层和下层进行训练的多项Logistic回归（softmax激活）。新的DNN代替使用支持向量机（SVM）作为一个或多个层，包括顶层。本文描述的技术可以使用两种训练算法中的一种来训练DNSVM以在最大裕度标准中学习SVM和DNN的参数。第一种训练方法是一个框架级的训练。在帧级训练中，新模型与具有DNN特征的多类SVM相关。第二种训练方法是序列级训练。序列级训练与具有DNN特征和HMM状态转换特征的结构化SVM相关。

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