1,535,914 research outputs found
Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers
Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. all). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner-M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that substantially improve on the simple Accuracy quality function that was previously used in Ant-Tree-Miner-M
Online Multi-task Learning with Hard Constraints
We discuss multi-task online learning when a decision maker has to deal
simultaneously with M tasks. The tasks are related, which is modeled by
imposing that the M-tuple of actions taken by the decision maker needs to
satisfy certain constraints. We give natural examples of such restrictions and
then discuss a general class of tractable constraints, for which we introduce
computationally efficient ways of selecting actions, essentially by reducing to
an on-line shortest path problem. We briefly discuss "tracking" and "bandit"
versions of the problem and extend the model in various ways, including
non-additive global losses and uncountably infinite sets of tasks
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
Most of the traditional convolutional neural networks (CNNs) implements
bottom-up approach (feed-forward) for image classifications. However, many
scientific studies demonstrate that visual perception in primates rely on both
bottom-up and top-down connections. Therefore, in this work, we propose a CNN
network with feedback structure for Solar power plant detection on
middle-resolution satellite images. To express the strength of the top-down
connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model
used for solar power plant classification on multi-spectral satellite data.
Moreover, we introduce a method to improve class activation mapping (CAM) to
our FB-Net, which takes advantage of multi-channel pulse coupled neural network
(m-PCNN) for weakly-supervised localization of the solar power plants from the
features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,
experimental results demonstrated promising results on both solar-power plant
image classification and detection task.Comment: 9 pages, 9 figures, 4 table
M-MRAC for Nonlinear Systems with Bounded Disturbances
This paper presents design and performance analysis of a modified reference model MRAC (M-MRAC) architecture for a class of multi-input multi-output uncertain nonlinear systems in the presence of bounded disturbances. M-MRAC incorporates an error feedback in the reference model definition, which allows for fast adaptation without generating high frequency oscillations in the control signal, which closely follows the certainty equivalent control signal. The benefits of the method are demonstrated via a simulation example of an aircraft's wing rock motion
Localization and Large N reduction on S^3 for the Planar and M-theory limit
We show a large N reduction on S^3 in a BPS sector for a broad class of
theories : N>=2 supersymmetric Chern-Simons theory with any number of adjoint
and bi-fundamental chiral multiplets.We show that a localization method can be
applied to the reduced model and the path integral can be written by a
multi-contour integral. By taking a particular localization configuration, we
also show that the large N equivalence between the original theory on S^3 and
the reduced model holds for the free energy and the expectation value of BPS
Wilson loops. It turns out that the large N reduction on S^3 holds also for the
M-theory limit.Comment: v1:39 pages, v2:40 pages, references added, typo fixe
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