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Quiz 1 on Wednesday

Quiz 1 on Wednesday

Quiz 1 on Wednesday. ~20 multiple choice or short answer questions In class, full period Only covers material from lecture, with a bias towards topics not covered by projects Study strategy: Review the slides and consult textbook to clarify confusing parts. Project 3 preview.

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599 views • 46 slides
A System for Understanding Imaged Infographics and Its Applications

A System for Understanding Imaged Infographics and Its Applications

A System for Understanding Imaged Infographics and Its Applications Weihua Huang, Chew Lim Tan School of Computing National University of Singapore Outline Introduction Syntactic and semantic information in scientific charts Chart recognition Chart interpretation Applications

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631 views • 31 slides
Chapter 8: Directed Graphs

Chapter 8: Directed Graphs

Chapter 8: Directed Graphs. digraph:. A digraph is a graph G = (V,E) where V is a finite set of vertices and E is a relation on V . If a, b ? V and aEb then there is a directed edge of E from a to b called an arc .

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408 views • 28 slides
CS1022 Computer Programming & Principles

CS1022 Computer Programming & Principles

CS1022 Computer Programming & Principles. Lecture 8.1 Digraphs (1). Plan of lecture. Digraphs (definition and terminology) Simple digraphs Paths and cycles PERT charts Topological sort algorithm. Digraphs, again. Directed graphs = digraphs We have used digraphs to represent relations

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419 views • 25 slides
Discriminative and generative methods for bags of features

Discriminative and generative methods for bags of features

Discriminative and generative methods for bags of features. Zebra. Non-zebra. Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba. Image classification.

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507 views • 22 slides
Image classification

Image classification

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing them?. Classifiers. Learn a decision rule assigning bag-of-features representations of images to different classes. Decision boundary. Zebra. Non-zebra.

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411 views • 21 slides

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