Exploiting separability in large-scale linear support vector machine training

Linear support vector machine training can be represented as a large quadratic program. We present an efficient and numerically stable algorithm for this problem using interior point methods, which requires only O(n) operations per iteration. Through exploiting the separability of the Hessian, we provide a unified approach, from an optimization perspective, to 1-norm classification, 2-norm classification, universum classification, ordinal regression and ɛ-insensitive regression. Our approach has the added advantage of obtaining the hyperplane weights and bias directly from the solver. Numerical experiments indicate that, in contrast to existing methods, the algorithm is largely unaffected by noisy data, and they show training times for our implementation are consistent and highly competitive. We discuss the effect of using multiple correctors, and monitoring the angle of the normal to the hyperplane to determine termination

Once a Batesian mimic, not always a Batesian mimic: mimic reverts back to ancestral phenotype when the model is absent

Batesian mimics gain protection from predation through the evolution of physical similarities to a model species that possesses anti-predator defences. This protection should not be effective in the absence of the model since the predator does not identify the mimic as potentially dangerous and both the model and the mimic are highly conspicuous. Thus, Batesian mimics should probably encounter strong predation pressure outside the geographical range of the model species. There are several documented examples of Batesian mimics occurring in locations without their models, but the evolutionary responses remain largely unidentified. A mimetic species has four alternative evolutionary responses to the loss of model presence. If predation is weak, it could maintain its mimetic signal. If predation is intense, it is widely presumed the mimic will go extinct. However, the mimic could also evolve a new colour pattern to mimic another model species or it could revert back to its ancestral, less conspicuous phenotype. We used molecular phylogenetic approaches to reconstruct and test the evolution of mimicry in the North American admiral butterflies (Limenitis: Nymphalidae). We confirmed that the more cryptic white-banded form is the ancestral phenotype of North American admiral butterflies. However, one species, Limenitis arthemis, evolved the black pipevine swallowtail mimetic form but later reverted to the white-banded more cryptic ancestral form. This character reversion is strongly correlated with the geographical absence of the model species and its host plant, but not the host plant distribution of L. arthemis. Our results support the prediction that a Batesian mimic does not persist in locations without its model, but it does not go extinct either. The mimic can revert back to its ancestral, less conspicuous form and persist

S.: Fast pattern selection for support vector classifiers, Lecture

Abstract. Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.

Competing tunneling and capacitive paths in Co-ZrO2 granular thin films

The ac electrical response is studied in thin films composed of well-defined nanometric Co particles embedded in an insulating ZrO2 matrix which tends to coat them, preventing the formation of aggregates. In the dielectric regime, ac transport originates from the competition between interparticle capacitive Cp and tunneling Rt channels, the latter being thermally assisted. This competition yields an absorption phenomenon at a characteristic frequency 1/(RtCp), which is observed in the range 1010 000 Hz. In this way, the effective ac properties mimic the universal response of disordered dielectric materials. Temperature and frequency determine the complexity and nature of the ac electrical paths, which have been successfully modeled by an Rt-Cp network