FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION Marius Muja, David G. Lowe Computer Science Department, University of British Columbia, Vancouver, B.C., Canada ,

Source URL: www.geog.ucsb.edu

• Search algorithms
• Information science
• Computing
• Computer programming
• Machine learning
• Data mining
• Approximation algorithms
• Discrete geometry
• Nearest neighbor search
• K-d tree
• K-nearest neighbors algorithm
• K-means clustering

An Ensemble Approach to Financial Entity Matching for the FEIII 2016 Challenge Enrico Palumbo Giuseppe Rizzo

Source URL: giusepperizzo.github.io

• Statistical classification
• Machine learning
• Information
• Statistics
• Naive Bayes classifier
• Naive
• Ensemble learning
• Precision and recall

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Coupled Generative Adversarial Nets Liu, M.-Y.; Tuzel, O. TR2016-070

Source URL: www.merl.com

• Machine learning
• Artificial intelligence
• Statistical models
• Learning
• Computational neuroscience
• Statistics
• Artificial neural networks
• Discriminative model
• Generative model
• Convolutional neural network
• Supervised learning
• MNIST database

Genetic Algorithms: A Tutorial “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating

Source URL: www.csd.uwo.ca

• Genetic algorithms
• Cybernetics
• Applied mathematics
• Mathematical optimization
• Search algorithms
• Cognitive science
• Numerical analysis
• Machine learning
• Genetic operator
• Crossover
• Chromosome
• Evolutionary computation

FINAL PROJECT INSTRUCTIONS MAS S.62: MIT Fall 2013 SCOPE & REQUIREMENTS You will complete a final project on a topic related to interactive machine learning. Requirements: 1) There should be significant attention to supp

Source URL: iml.media.mit.edu

• Education
• Human communication
• Humancomputer interaction
• Interpersonal communication
• Educational psychology
• Distance education
• Learning
• Educational technology
• Evaluation
• Machine learning
• Statistical classification
• User interface

A Science Model Driven Retrieval Prototype1 Philipp Mayr GESIS – Leibniz-Institute for the Social Sciences Lennéstr. 30, 53113 Bonn, Germany

Source URL: www.ib.hu-berlin.de

• Information science
• Information retrieval
• Information retrieval evaluation
• Natural language processing
• Machine learning
• Internet search engines
• Relevance
• Ranking
• Query expansion
• Precision and recall
• Search engine technology
• Tfidf

Microsoft PowerPoint - QDB-ScalableMatching.pptx

Source URL: dbs.uni-leipzig.de

• Data management
• Record linkage
• Support vector machine
• Machine learning
• SVM
• Cognition
• Cognitive science

MACHINE LEARNING BASED POWER ANALYSIS Attacks Presenter: Yun-Wen Lu Advisor: Chen-Yi Lee Hsie-Chia Chang Motivation

Source URL: diamondprj.nctu.edu.tw

Multi Modal Laughter Detection in Natural Discourses Stefan Scherer, Friedhelm Schwenker, and Nick Campbell University of Ulm, NiST, ATR Abstract. ABSTRACT

Source URL: www.speech-data.jp

• Machine learning
• Statistical classification
• Support vector machine
• Pattern recognition
• Laughter
• Image segmentation
• Cognition
• Cognitive science
• Structure

DCU-ADAPT: Learning Edit Operations for Microblog Normalisation with the Generalised Perceptron Joachim Wagner and Jennifer Foster ADAPT Centre School of Computing Dublin City University

Source URL: noisy-text.github.io

• Artificial intelligence
• Computational linguistics
• Machine learning
• Learning
• Artificial neural networks
• Natural language processing
• Compiler construction
• Perceptron
• N-gram
• Deep learning
• Parsing
• Speech recognition