Large Margin Classifier Based on Affine Hulls

2010 ◽  
Author(s):  
Hakan Cevikalp ◽  
Hasan Serhan Yavuz
Keyword(s):  
Large Margin ◽  
Affine Hulls

scholarly journals Large margin classifiers based on affine hulls

2010 ◽  
Vol 73 (16-18) ◽  
pp. 3160-3168 ◽  
Author(s):  
Hakan Cevikalp ◽  
Bill Triggs ◽  
Hasan Serhan Yavuz ◽  
Yalçın Küçük ◽  
Mahide Küçük ◽  
...  
Keyword(s):  
Large Margin ◽  
Large Margin Classifiers ◽  
Affine Hulls

An Experiment to Measure the Performance of Phonetic Key Compression Retrieval Schemes

1977 ◽  
Vol 16 (04) ◽  
pp. 230-233 ◽  
Author(s):  
R. H. Greenfield
Keyword(s):  
Large Margin ◽  
Patient File

The results of an experiment to measure the performance of the Davidson and Soundex phonetic key compression schemes in finding sets of records representing the same individual in a moderately large radiology patient file, as compared to exact surname matches, are presented. This is similar to the problem of retrieving a record by name under the assumption that neither the search key nor the recorded key is accurately known. Both phonetic schemes perform similarly in obtaining extra matches and both outperform by a large margin the exact, name match. The results also indicate that the Davidson scheme is superior to the Soundex because it produces significantly fewer mismatches.

Multi-Resolution Autoregressive Graph-to-Graph Translation for Molecules

2019 ◽  
Author(s):  
Wengong Jin ◽  
Regina Barzilay ◽  
Tommi S Jaakkola
Keyword(s):  
Drug Discovery ◽  
State Of The Art ◽  
Molecular Graph ◽  
Biochemical Properties ◽  
Large Margin ◽  
Previous State ◽  
Translation Methods ◽  
Atom Level ◽  
Precursor Molecules ◽  
Prior State

The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving trees over substructures with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its connectivity to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines by a large margin.

Large Margin Training for Attention Based End-to-End Speech Recognition

2019 ◽  
Author(s):  
Peidong Wang ◽  
Jia Cui ◽  
Chao Weng ◽  
Dong Yu
Keyword(s):  
Speech Recognition ◽  
Large Margin ◽  
End To End
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