This slide explain about identification between various points cloud that is generated by Leaser scanning. The identification is made by ICP(Interactive Closed Point) which uses SVD method.
Presented by Christian Moen, Founder and CEO Atilika Inc - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
This talk gives an introduction to searching Japanese text and an overview of the new Japanese search features available out-of-the-box in Lucene and Solr.
Atilika developed a new Japanese morphological analyzer (Kuromoji) in 2010 when they couldn't find any easy-to-use, high-quality morphological analyzer in Java that was good for both search and other Japanese NLP tasks. Kuromoji was built with the goal of donating it to the Apache Software Foundation in order to make Japanese work well for both Lucene and Solr, and is now a standard part of these software packages.
論文紹介:
Pan, Wei-Xing, et al. "Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network." The Journal of neuroscience 25.26 (2005): 6235-6242.
Li, Mu, et al. "Efficient mini-batch training for stochastic optimization." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.
http://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf
KDD2014勉強会関西会場: http://www.ml.ist.i.kyoto-u.ac.jp/kdd2014reading
Sotetsu Koyamada (Presenter), Masanori Koyama, Ken Nakae, Shin Ishii
Graduate School of Informatics, Kyoto University
[Abstract]
We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined k-th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA’s ability to decompose the knowledge acquired by the trained classifiers.
[Keywords]
Sensitivity analysis Sensitivity map PCA Dark knowledge Knowledge decomposition
@PAKDD2015
May 20, 2015
Ho Chi Minh City, Viet Namﳟ
http://link.springer.com/chapter/10.1007%2F978-3-319-18038-0_48#page-1
This slide explain about identification between various points cloud that is generated by Leaser scanning. The identification is made by ICP(Interactive Closed Point) which uses SVD method.
Presented by Christian Moen, Founder and CEO Atilika Inc - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
This talk gives an introduction to searching Japanese text and an overview of the new Japanese search features available out-of-the-box in Lucene and Solr.
Atilika developed a new Japanese morphological analyzer (Kuromoji) in 2010 when they couldn't find any easy-to-use, high-quality morphological analyzer in Java that was good for both search and other Japanese NLP tasks. Kuromoji was built with the goal of donating it to the Apache Software Foundation in order to make Japanese work well for both Lucene and Solr, and is now a standard part of these software packages.
論文紹介:
Pan, Wei-Xing, et al. "Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network." The Journal of neuroscience 25.26 (2005): 6235-6242.
Li, Mu, et al. "Efficient mini-batch training for stochastic optimization." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.
http://www.cs.cmu.edu/~muli/file/minibatch_sgd.pdf
KDD2014勉強会関西会場: http://www.ml.ist.i.kyoto-u.ac.jp/kdd2014reading
Sotetsu Koyamada (Presenter), Masanori Koyama, Ken Nakae, Shin Ishii
Graduate School of Informatics, Kyoto University
[Abstract]
We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined k-th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA’s ability to decompose the knowledge acquired by the trained classifiers.
[Keywords]
Sensitivity analysis Sensitivity map PCA Dark knowledge Knowledge decomposition
@PAKDD2015
May 20, 2015
Ho Chi Minh City, Viet Namﳟ
http://link.springer.com/chapter/10.1007%2F978-3-319-18038-0_48#page-1
Reinforcement Learning with few reward is challenge subject. This slide provides same method for reinforce learning with few reward and some latent variable model by VAE.
This slides explain about scanning picture feature points that is made by SIFT(Scale Invariant Feature Transform) which uses Gaussian Filter Difference Logic (DoG).