Classification and automatic recognition of objects using H2o package (Conference Presentation)

Yenumula B. Reddy

doi:10.1117/12.2266596

7 June 2017 Classification and automatic recognition of objects using H2o package(Conference Presentation)

Yenumula B. Reddy

Proceedings Volume 10185, Cyber Sensing 2017; 101850F (2017) https://doi.org/10.1117/12.2266596
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Deep learning (DL) is a set of methods that automatically classify the raw data fed into the machine. Deep Convolutional nets composed of multiple processing layers to learn and representation of data with multiple levels of abstraction to process images, video, speech and audio. H2o deep learning architecture has many features that include supervised training protocol, memory efficient Java implementation, adaptive learning, and with related CRAN packages. H2o uses supervised training protocol with a uniform adaptive option which is an optimization based on the size of the network. It can take clusters of computing nodes to train on the entire data set but automatically shuffling the training examples for each iteration locally. The framework supports regularization techniques to prevent overfitting. H2o R has intuitive web interface using localhost and IP address. Using the H2o package in R is easy. The computations are performed in the H2o cluster and initiated by REST calls (in highly optimized Java code) from R. Since SPARK is available in R, H2o uses a single R session and communicates to the H2o Java cluster via REST calls. H2o runs inside the Spark executor JVM. Using these packages in R, we demonstrate the classification and automatic recognition of objects. Further, we use the h2o deep learning package in R Language to classify the NOAA VIIRS Night fires data to detect the persistent fire activity at a given location around the globe.

Conference Presentation

Citation Download Citation

Yenumula B. Reddy "Classification and automatic recognition of objects using H2o package(Conference Presentation)", Proc. SPIE 10185, Cyber Sensing 2017, 101850F (7 June 2017); https://doi.org/10.1117/12.2266596

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