15,763 research outputs found
Efficient Decomposed Learning for Structured Prediction
Structured prediction is the cornerstone of several machine learning
applications. Unfortunately, in structured prediction settings with expressive
inter-variable interactions, exact inference-based learning algorithms, e.g.
Structural SVM, are often intractable. We present a new way, Decomposed
Learning (DecL), which performs efficient learning by restricting the inference
step to a limited part of the structured spaces. We provide characterizations
based on the structure, target parameters, and gold labels, under which DecL is
equivalent to exact learning. We then show that in real world settings, where
our theoretical assumptions may not completely hold, DecL-based algorithms are
significantly more efficient and as accurate as exact learning.Comment: ICML201
RNA secondary structure prediction using large margin methods
The secondary structure of RNA is essential for its biological role. Recently, Do, Woods, Batzoglou, (ISMB 2006) proposed a probabilistic approach that generalizes SCFGs using conditional maximum likelihood to estimate the model parameters. We propose an alternative approach to parameter estimation which is based on an SVM-like large margin method
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