Transcription of Deep Learning using Linear Support Vector Machines
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Deep Learning using Linear Support Vector Machines
deep Learning using Linear Support Vector MachinesYichuan of Computer Science, University of Toronto. Toronto, Ontario, , fully-connected and convolutionalneural networks have been trained to achievestate-of-the-art performance on a wide vari-ety of tasks such as speech recognition, im-age classification, natural language process-ing, and bioinformatics. For classificationtasks, most of these deep Learning modelsemploy the softmax activation function forprediction and minimize cross-entropy this paper, we demonstrate a small butconsistent advantage of replacing the soft-max layer with a Linear Support Vector ma-chine. Learning minimizes a margin-basedloss instead of the cross-entropy loss.
Deep Learning using Linear Support Vector Machines 2. The model 2.1. Softmax For classi cation problems using deep learning tech-niques, it is standard to use the softmax or 1-of-K
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