Transcription of Deep Learning using Linear Support Vector Machines
{{id}} {{{paragraph}}}
Example: bankruptcy
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. Whilethere have been various combinations of neu-ral nets and SVMs in prior art, our resultsusing L2-SVMs show that by simply replac-ing softmax with Linear SVMs gives signifi-cant gains on popular deep Learning datasetsMNIST, CIFAR-10, and the ICML 2013 Rep-resentation Learning Workshop s face expres-sion recognition IntroductionDeep Learning using neural networks have claimedstate-of-the-art performances in a wide range of include (but not limited to) speech (Mohamedet al.)
Deep Learning using Linear Support Vector Machines neural nets for classi cation. Lower layer weights are learned by backpropagating the gradients from the top
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
An Introduction to Deep Learning for, Embedded low-power deep learning with TIDL, Introduction, New End: New Pedagogies for Deep, New End: New Pedagogies for Deep Learning, Deep Learning, Learning, Activity Title: Introduction to Ocean Zones, Activity Title: Introduction to Ocean Zones Learning, RESEARCH INTO IDENTIFYING EFFECTIVE, RESEARCH INTO IDENTIFYING EFFECTIVE LEARNING ENVIRONMENTS