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Feature learning
Results: 920

#	Item
171	Deep Features to Classify Skin Lesions Jeremy Kawahara, A¨ıcha BenTaieb, and Ghassan Hamarneh Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada ABSTRACT Diagnosing an unknown skin Add to Reading List Source URL: www.cs.sfu.ca Language: English - Date: 2016-02-06 13:56:21 RTT Computational neuroscience Computer vision Convolutional neural network Deep learning ImageNet Melanoma Dermatoscopy Support vector machine Image segmentation K-nearest neighbors algorithm Feature
172	Computer Vision Exercise Session 10 – Image Categorization Object Categorization  Task Description Add to Reading List Source URL: www.cvg.ethz.ch Language: English - Date: 2015-12-15 06:45:03 Feature detection Machine learning Bag-of-words model in computer vision Histogram of oriented gradients Feature Classifier Visual Word Bag-of-words model Statistical classification Boosting Object categorization from image search
173	Feature: What Keeps Principals Up At Night? Over the past two decades, the responsibilities placed upon principals have grown, and yet their role has never been more vital to our students’ future and that of our nation Add to Reading List Source URL: www.nmefoundation.org Language: English - Date: 2015-11-16 22:01:48 Pedagogy Social networking services Educational psychology Educational practices Alternative education Student voice Facebook Personalized learning Twitter Educational technology National Board for Professional Teaching Standards Blended learning
174	Published as a conference paper at ICLRA DAM : A M ETHOD FOR S TOCHASTIC O PTIMIZATION Diederik P. Kingma* University of Amsterdam Add to Reading List Source URL: arxiv.org Language: English - Date: 2015-07-26 20:01:45 M-estimators Computational statistics Computational neuroscience Estimation theory Stochastic optimization Stochastic gradient descent Deep learning Artificial neural network Convolutional neural network Mathematical optimization Loss function Feature learning
175	REVIEW doi:nature14539 Deep learning Yann LeCun1,2, Yoshua Bengio3 & Geoffrey Hinton4,5 Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2015-08-10 12:54:32 Artificial neural networks Deep learning Convolutional neural network Recurrent neural network Backpropagation Autoencoder Feature learning Recursive neural network Long short-term memory Perceptron Feedforward neural network Artificial intelligence
176	Feature: Learners In The Center, Not Learners On Their Own “The goal of learner-centered education is to create the conditions so that students can be self-motivated to engage in learning.” Here’s how to encourage Add to Reading List Source URL: www.nmefoundation.org Language: English - Date: 2015-12-02 00:02:45 Social networking services Educational psychology Pedagogy Educational practices Personalized learning PARCC Facebook Twitter Educational technology
177	What matters: Size does, Smarts Don’t Albrecht Zimmermann and Bj¨orn Bringmann Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium {Albrecht.Zimmermann,Bjorn.Brin Add to Reading List Source URL: www.ke.tu-darmstadt.de Language: English - Date: 2009-11-06 11:55:58 Model selection Machine learning Formal sciences Statistical classification Cross-validation Support vector machine Data mining Feature selection DM
178	A Model of Zero-Shot Learning of Spoken Language Understanding Majid Yazdani Computer Science Department University of Geneva Add to Reading List Source URL: majid.yazdani.me Language: English - Date: 2015-08-19 06:58:41 Statistical classification Machine learning Artificial neural networks Support vector machine Word embedding Feature learning Multi-label classification Word2vec Margin classifier Language model
179	AUTOMATIC MAP FEATURE EXTRACTION USING ARTIFICIAL NEURAL NETWORKS Itthi Trisirisatayawong and Mark R. Shortis Department of Geomatics University of Melbourne Parkville VIC 3052 AUSTRALIA Add to Reading List Source URL: mapcontext.com Language: English - Date: 2008-08-29 22:23:47 Machine learning Artificial neural networks Computational neuroscience Computational statistics Mathematical psychology Pattern recognition Backpropagation Statistical classification Image segmentation Supervised learning Deep learning Book:Machine Learning The Complete Guide
180	A SUPERVISED APPROACH FOR DETECTING BOUNDARIES IN MUSIC USING DIFFERENCE FEATURES AND BOOSTING Douglas Turnbull1 , Gert Lanckriet2 Computer Science & Engineering1 Electrical & Computer Engineering2 University of Californ Add to Reading List Source URL: eceweb.ucsd.edu Language: English - Date: 2015-07-31 19:00:26 Music information retrieval Machine learning Timefrequency analysis Statistical classification Chroma feature Music technology Image segmentation Mel-frequency cepstrum AdaBoost Linear classifier Supervised learning Diff utility

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