PPT-Similarity Learning in

Financial Services Dhagash Mehta BlackRock Inc Disclaimer The views expresses here are those of the authors alone and not of BlackRock Inc Introduction Similarity Scene from Alices Adventures in Wonderland by Lewis Carroll 1865 Artist John Tenniel. Giulio Finestrali. CSE 435 – Intelligent Decision Support Systems. Instructor: Prof. Hector Muñoz-Avila. Lehigh University – Fall 2012. Introduction. The notion of Knowledge . Containers was introduced. Given:. A query image. A database of images with known locations. Two types of approaches:. Direct matching. : directly match image features to 3D points (high memory requirement). Retrieval based. : retrieve a short list of most similar images and perform image matching. from . GOMMA. Michael . Hartung. , Lars Kolb, . Anika. . Groß. , Erhard Rahm. Database . Research Group. University of . Leipzig. 9th . Intl. . . Conf. . on Data Integration. in . the. Life . Sciences. these theories have explanatory power domains partially role of relational judgments. Previous structural and aspects of notion of relational similarity by the fact that and her some ways there is in Ph.D. Thesis Defense. Anoop Cherian. *. Department of Computer Science and Engineering. University of Minnesota, Twin-Cities. Adviser. : Prof. Nikolaos Papanikolopoulos. *Contact: . cherian@cs.umn.edu. Warm Up. 1.. . If . ∆. QRS.  . ∆. ZYX. , identify the pairs of congruent angles and the pairs of congruent sides.. Solve each proportion.. 2.. . 3.. . . x . = 9. x . = 18. . Q. Moitreya Chatterjee, . Yunan. . Luo. Image Source: Google. Outline – This Section. Why do we need Similarity Measures. Metric Learning as a measure of Similarity. Notion of a metric. Unsupervised Metric Learning. Case-based reasoning. Introduction. Common term in everyday language, where two objects usually are considered similar if they look or sound similar. Similarity is a core concept within CBR. From a CBR perspective: «Two problems are similar if they have similar solutions». CS159 . Fall . 2014. Admin. Assignment 4. Quiz #2 Thursday. Same . rules as quiz #1. First 30 minutes of class. Open book and . notes. Assignment 5 out on Thursday. Quiz #2. Topics. Linguistics 101. Parsing. Moitreya Chatterjee, . Yunan. . Luo. Image Source: Google. Outline – This Section. Why do we need Similarity Measures. Metric Learning as a measure of Similarity. Notion of a metric. Unsupervised Metric Learning. Presenter. : Monica Farkash. Bryan Hickerson. . mfarkash@us.ibm.com. . bhickers@us.ibm.com. . 2. Outline. The challenge: Providing a subset from a regression test suite. Our new Jaccard/K-means (JK) approach . Text Similarity. Motivation. People can express the same concept (or related concepts) in many different ways. For example, “the plane leaves at 12pm” vs “the flight departs at noon”. Text similarity is a key component of Natural Language Processing. Quiz. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. 3. Deer-mouse. 4. Deer-roof. Quiz Answer. Which pair of words exhibits the greatest similarity?. 1. Deer-elk. 2. Deer-horse. Scott Wen-tau Yih. Joint work with. Kristina Toutanova, John Platt, Chris Meek. Microsoft Research. Cross-language Document Retrieval. English Query Doc. Spanish Document Set. Web Search & Advertising.