422,620 research outputs found
Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines
Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods
Structure Learning of Partitioned Markov Networks
We learn the structure of a Markov Network between two groups of random
variables from joint observations. Since modelling and learning the full MN
structure may be hard, learning the links between two groups directly may be a
preferable option. We introduce a novel concept called the \emph{partitioned
ratio} whose factorization directly associates with the Markovian properties of
random variables across two groups. A simple one-shot convex optimization
procedure is proposed for learning the \emph{sparse} factorizations of the
partitioned ratio and it is theoretically guaranteed to recover the correct
inter-group structure under mild conditions. The performance of the proposed
method is experimentally compared with the state of the art MN structure
learning methods using ROC curves. Real applications on analyzing
bipartisanship in US congress and pairwise DNA/time-series alignments are also
reported.Comment: Camera Ready for ICML 2016. Fixed some minor typo
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