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Predicting Structured Objects with Support Vector Machines
DOI:10.1145/1592761.1592783.png)
摘要
En 中文
Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for such complex prediction problems.
Keyword:
PROTEIN-STRUCTURE ALIGNMENT
AI总结
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期刊
IF:
12.2
论文数:
1.2W
被引数:
3.7W
机构
引用论文
SSALN: An alignment algorithm using structure-dependent substitution matrices and gap penalties learned from structurally aligned protein pairsSSALN: 一种使用结构依赖的替换矩阵和从结构对齐的蛋白质对中学习的缺口惩罚的比对算法
TM-align: a protein structure alignment algorithm based on the TM-scoreTm-align: 一种基于tm-score的蛋白质结构比对算法
NUCLEIC ACIDS RESEARCH
IF13.1

