Business Administration 1.1Identify the features of different types of business organisations.
Different Features
description
Transcript of Different Features
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Different FeaturesDifferent Features
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Glasses vs. No GlassesGlasses vs. No Glasses
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Beard vs. No BeardBeard vs. No Beard
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Beard DistinctionBeard Distinction
Ghodsi et, al 2007
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Glasses DistinctionGlasses Distinction
Ghodsi et, al 2007
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Multiple-Attribute MetricMultiple-Attribute Metric
Ghodsi et, al 2007
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Embedding of sparse music Embedding of sparse music similarity graphsimilarity graph
Platt, 2004
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Reinforcement learningReinforcement learning
Mahadevan and Maggioini, 2005
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Semi-supervised learningSemi-supervised learning
Use graph-based discretization of manifold to infer missing labels.
Build classifiers from bottom eigenvectors of graph Laplacian.
Belkin & Niyogi, 2004; Zien et al, Eds., 2005
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correspondencescorrespondences
http://www.bushorchimp.com
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Learning correspondencesLearning correspondences
How can we learn manifold structure that is shared across multiple data sets?
c et al, 2003, 2005
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Mapping and robot localizationMapping and robot localization
Bowling, Ghodsi, Wilkinson 2005
Ham, Lin, D.D. 2005
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ClassificationClassification
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ClassificationClassification
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DataData
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Features (X)
(Green, 6, 4, 4.5)
(Green, 7, 4.5, 5)
(Red, 6, 3, 3.5)
(Red, 4.5, 4, 4.5)
(Yellow, 1.5, 8, 2)
(Yellow, 1.5, 7, 2.5)
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Data RepresentationData Representation
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Data RepresentationData Representation
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11 11 11 11 11
11 00 11 00 11
11 11 11 11 11
11 0.50.5 0.50.5 0.50.5 11
11 11 11 11 11
Data RepresentationData Representation
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Features and labelsFeatures and labels
(Green, 6, 4, 4.5)
(Green, 7, 4.5, 5)
(Red, 6, 3, 3.5)
(Red, 4.5, 4, 4.5)
(Yellow, 1.5, 8, 2)
(Yellow, 1.5, 7, 2.5)
Green Pepper
Green Pepper
Red Pepper
Red Pepper
Hot Pepper
Hot Pepper
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Features and labelsFeatures and labels
Objects Features (X) Labels (Y)
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Classification (New point)Classification (New point)
(Red, 7, 4, 4.5)h(Red, 7, 4, 4.5)
?
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Classification (New point)Classification (New point)
(Red, 5, 3, 4.5)h(Red, 5, 3, 4.5)
?
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Digit RecognitionDigit Recognition
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ClassificationClassification
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ClassificationClassification
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ClassificationClassification
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ClassificationClassification
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Computer VisionComputer Vision
N. Jojic and B.J. Frey, “ Learning flexible sprites in video layers”, CVPR 2001, (Video)
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ReadingReading
• Journals: Neural Computation, JMLR, ML, IEEE PAMI• Conferences: NIPS, UAI, ICML, AI-STATS, IJCAI,
IJCNN• Vision: CVPR, ECCV, SIGGRAPH• Speech: EuroSpeech, ICSLP, ICASSP• Online: citesser, google• Books:
– Elements of Statistical Learning, Hastie, Tibshirani, Friedman– Learning from Data, Cherkassky, Mulier– Pattern classification, Duda, Hart, Stork– Neural Networks for pattern Recognition, Bishop– Pattern recognition and machine learning, Bishop