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The Problem: Consider a two class task with ? 1 , ? 2

LINEAR CLASSIFIERS. The Problem: Consider a two class task with ? 1 , ? 2. Hence:. The Perceptron Algorithm Assume linearly separable classes, i.e., The case falls under the above formulation, since. Our goal: Compute a solution, i.e., a hyperplane w , so that The steps

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637 views • 53 slides

Data Mining in Practice: Techniques and Practical Applications

Data Mining in Practice: Techniques and Practical Applications . Junling Hu May 14, 2013. What is data mining?. Mining patterns from data Is it statistics? Functional form? Computation speed concern? Data size Variable size Is it machine learning? Big data issue

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580 views • 44 slides

Human Detection

Human Detection . Mikel Rodriguez. Organization. 1. Moving Target Indicator (MTI) Background models Moving region detection Target chip generation Results. 2. Target Classification (Human Detection) Target features Support vector machines Results. Input Frame. Object Detection.

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657 views • 33 slides

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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423 views • 27 slides

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'Maximize margin' diaporamas de présentation

Maximize margin - PowerPoint PPT Presentation

The Problem: Consider a two class task with ? 1 , ? 2

Data Mining in Practice: Techniques and Practical Applications

Human Detection

Image classification