Bridge detection in grid terrains and improved drainage enforcement Ryan Carlson Andrew Danner

Source URL: www.cs.swarthmore.edu

• Computer vision
• Learning
• AdaBoost
• Digital elevation model
• Classifier
• Boosting
• USGS DEM
• Statistical classification
• LIDAR
• Artificial intelligence
• Ensemble learning
• Machine learning

Comparison of Classifier Selection Methods for Improving Committee Performance Matti Aksela Helsinki University of Technology, Neural Networks Research Centre P.O.Box 5400, FinHUT, Finland

Source URL: www.cis.hut.fi

• Parts of speech
• Statistical classification
• Ensemble learning
• Learning classifier system
• Classifier
• Support vector machine
• Chinese classifier
• Random subspace method
• Machine learning
• Statistics
• Artificial intelligence

Ensemble Nystr¨om Sanjiv Kumar, Mehryar Mohri and Ameet Talwalkar A common problem in many areas of large-scale machine learning involves manipulation of a large matrix. This matrix may be a kernel matrix arising in Sup

Source URL: www.sanjivk.com

An Empirical Comparison of Supervised Learning Algorithms Rich Caruana Alexandru Niculescu-Mizil Department of Computer Science, Cornell University, Ithaca, NYUSA

Source URL: www.niculescu-mizil.org

• Ensemble learning
• Bayesian statistics
• Neural networks
• Supervised learning
• Support vector machine
• Naive Bayes classifier
• Boosting
• Random forest
• Calibration
• Statistics
• Machine learning
• Statistical classification

Boosted Optimization for Network Classification Timothy Hancock Bioinformatics Center Institute for Chemical Research Kyoto University, Japan

Source URL: jmlr.csail.mit.edu

• Graph theory
• Ensemble learning
• Formal sciences
• Statistical models
• Belief propagation
• Gradient boosting
• AdaBoost
• Pattern recognition
• Bayesian network
• Statistics
• Graphical models
• Machine learning

Under review as a conference paper at ICLRT RAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING Scott E. Reed & Honglak Lee Dept. of Electrical Engineering and Computer Science, University of Michigan

Source URL: www-personal.umich.edu

• Statistical inference
• Ensemble learning
• Resampling
• Bootstrapping
• Semi-supervised learning
• Supervised learning
• Bootstrap aggregating
• Cross-validation
• Neural network
• Statistics
• Machine learning
• Computational statistics

Poster: Automatically Evading Classifiers A Case Study on Structural Feature-based PDF Malware Classifiers Weilin Xu, Yanjun Qi, and David Evans University of Virginia {xuweilin, yanjun, evans}@virginia.edu Abstract—Ma

Source URL: www.ieee-security.org

• Machine learning
• Artificial intelligence
• Malware
• Classifier
• Learning
• Support vector machine
• Ensemble learning
• Statistics
• Statistical classification
• Parts of speech

On Adaptive Confidences for Critic-Driven Classifier Combining Matti Aksela and Jorma Laaksonen Neural Networks Research Centre Laboratory of Computer and Information Science P.O.Box 5400, FinHUT, Finland

Source URL: www.cis.hut.fi

• Learning
• Statistical classification
• Parts of speech
• Classifier
• Ensemble learning
• Gaussian function
• Pattern recognition
• Linear classifier
• Learning classifier system
• Machine learning
• Statistics
• Artificial intelligence

Domain Adaptation with Good Edit Similarities: a Sparse Way to deal with Scaling and Rotation Problems in Image Classification Amaury Habrard University of Aix-Marseille Laboratoire d’Informatique Fondamentale UMR CNRS

Source URL: laboratoirehubertcurien.fr

• Learning
• Machine learning
• Ensemble learning
• Support vector machine
• AdaBoost
• Levenshtein distance
• Linear classifier
• Statistics
• Statistical classification
• Artificial intelligence

Crime Forecasting Using Spatio-Temporal Pattern with Ensemble Learning Chung-Hsien Yu1 , Wei Ding1 , Ping Chen1 , and Melissa Morabito2 1 University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA 02125, USA

Source URL: www.cs.umb.edu