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Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience
Physical symbol systems are needed for open-ended cognition. A good way to
understand physical symbol systems is by comparison of thought to chemistry.
Both have systematicity, productivity and compositionality. The state of the
art in cognitive architectures for open-ended cognition is critically assessed.
I conclude that a cognitive architecture that evolves symbol structures in the
brain is a promising candidate to explain open-ended cognition. Part 2 of the
paper presents such a cognitive architecture.Comment: Darwinian Neurodynamics. Submitted as a two part paper to Living
Machines 2013 Natural History Museum, Londo
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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.
Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have been
proposed in the literature, the question of how to aggregate the predictions of
the individual members of the ensemble has received little attention so far. In
this paper, we introduce a formal framework of ensemble multilabel
classification, in which we distinguish two principal approaches: "predict then
combine" (PTC), where the ensemble members first make loss minimizing
predictions which are subsequently combined, and "combine then predict" (CTP),
which first aggregates information such as marginal label probabilities from
the individual ensemble members, and then derives a prediction from this
aggregation. While both approaches generalize voting techniques commonly used
for multilabel ensembles, they allow to explicitly take the target performance
measure into account. Therefore, concrete instantiations of CTP and PTC can be
tailored to concrete loss functions. Experimentally, we show that standard
voting techniques are indeed outperformed by suitable instantiations of CTP and
PTC, and provide some evidence that CTP performs well for decomposable loss
functions, whereas PTC is the better choice for non-decomposable losses.Comment: 14 pages, 2 figure
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