PPT-CS 478 - Instance Based Learning

1 Nearest Neighbor Learning Classify based on local similarity Ranges from simple nearest neighbor to casebased and analogical reasoning Use local information near the current query instance to decide the classification of that instance. Outline. Motivation. Multiple Instance Learning (MIL). Diverse Density. Single Point Concept. Disjunctive Point Concept. SVM Algorithms for MIL. Single Instance Learner (SIL). Sparse MIL. mi-SVM. MI-SVM. Zhimin. He. iTechs. – ISCAS. 2013-03-21. Agenda. What’s Concept Drift. Causes of a Concept Drift. Types of Concept Drift. Detecting and Handling Concept Drift. Implications for Software Engineering Research. Jeremy . Bolton, . Seniha. . Yuksel. , Paul . Gader. CSI. Laboratory . University of Florida. Highlights. Hidden Markov Models (HMMs) are useful tools for landmine detection in GPR imagery. Explicitly incorporating the Multiple Instance Learning (MIL) paradigm in HMM learning is intuitive and effective. Geo-Resources and Environment. Lab, Bordeaux INP (. Bordeaux Institute of Technology. ), France. Supervisor. : . Samia . BOUKIR. CLASSIFICATION OF SATELLITE IMAGES USING MARGIN-BASED ENSEMBLE METHODS. APPLICATION TO LAND COVER MAPPING OF NATURAL SITES . Skills. Jacey Greece, DSc, MPH. Department of Community Health Sciences. BU School of Public Health. jabloom@bu.edu. Boston University Instructional Innovation Conference. March 7, 2014. Outline. Innovation Overview. Galaxy Bazaar. A social entrepreneurship venture. Bijal. . Damani. India. Does this represent YOUR classroom??. Want to turn it into something like THIS????. Many teachers did it……so can you through…... Zhe Jiang. zjiang@cs.ua.edu. Colocation Pattern and Examples. Colocation: a . set of . spatial features . that . frequently occur . in . together. . Example:. Ecology: symbiotic relationship in animals or plants. and Data Analytics. Yolanda Gil. University of Southern California. gil@isi.edu. Last Updated:. September 2016. ACI-1355475. CC-BY. Attribution . http. ://www.datascience4all.org . Introduction . nearest neighbor. Probabilistic models:. Naive Bayes. Logistic Regression. Linear models:. Perceptron. SVM. Decision models:. Decision Trees. Boosted Decision Trees. Random Forest. Outline: . a toolbox of useful algorithms concepts. , . Eran. . Tromer. , . Hovav. . Shacham. , and Stefan Savage, . Proceedings of the ACM Conference on Computer and Communications Security, Chicago, IL, November 2009.. Hey, You, get Off of My Cloud: . Presented by: Dr. Jan Vanderpool . Email: vanderj@wlac.edu. Building a Community of Learners. Within the community of learners, students and facilitator co-construct knowledge through active learning and participatory experiences.. Clustering, Dimensionality Reduction and Instance Based Learning Geoff Hulten Supervised vs Unsupervised Supervised Training samples contain labels Goal: learn All algorithms we’ve explored: Logistic regression Project-ApproachRevised March 10 2021Page 3of 5How Can This Be CrossCurricularThis question is phrased so that students must consider the place about which they are creating the product and the audien Slides for Chapter . 2, . Input: concepts, instances, attributes. . 2. Input: concepts, instances, attributes. Components of the input for learning. What’s a concept?. Classification, association, clustering, numeric prediction.