21,664 research outputs found
Committee Machine Networks to Diagnose Cardiovascular Diseases
A parallel committee machines technique for neural network systems with back propagation together with a majority voting scheme is presented in this paper. Previous research with regards to predict the presence of cardiovascular diseases has shown accuracy rates up to 72.9% but it comes with a cost of reduced prediction accuracy of the minority class. The designed neural network system in this article presents a significant increase of robustness and it is shown that by majority voting of the parallel networks, recognition rates reach to > 90 in the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data set
A Latent Source Model for Nonparametric Time Series Classification
For classifying time series, a nearest-neighbor approach is widely used in
practice with performance often competitive with or better than more elaborate
methods such as neural networks, decision trees, and support vector machines.
We develop theoretical justification for the effectiveness of
nearest-neighbor-like classification of time series. Our guiding hypothesis is
that in many applications, such as forecasting which topics will become trends
on Twitter, there aren't actually that many prototypical time series to begin
with, relative to the number of time series we have access to, e.g., topics
become trends on Twitter only in a few distinct manners whereas we can collect
massive amounts of Twitter data. To operationalize this hypothesis, we propose
a latent source model for time series, which naturally leads to a "weighted
majority voting" classification rule that can be approximated by a
nearest-neighbor classifier. We establish nonasymptotic performance guarantees
of both weighted majority voting and nearest-neighbor classification under our
model accounting for how much of the time series we observe and the model
complexity. Experimental results on synthetic data show weighted majority
voting achieving the same misclassification rate as nearest-neighbor
classification while observing less of the time series. We then use weighted
majority to forecast which news topics on Twitter become trends, where we are
able to detect such "trending topics" in advance of Twitter 79% of the time,
with a mean early advantage of 1 hour and 26 minutes, a true positive rate of
95%, and a false positive rate of 4%.Comment: Advances in Neural Information Processing Systems (NIPS 2013
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
EnsembleSVM is a free software package containing efficient routines to
perform ensemble learning with support vector machine (SVM) base models. It
currently offers ensemble methods based on binary SVM models. Our
implementation avoids duplicate storage and evaluation of support vectors which
are shared between constituent models. Experimental results show that using
ensemble approaches can drastically reduce training complexity while
maintaining high predictive accuracy. The EnsembleSVM software package is
freely available online at http://esat.kuleuven.be/stadius/ensemblesvm.Comment: 5 pages, 1 tabl
Robustness Verification of Support Vector Machines
We study the problem of formally verifying the robustness to adversarial
examples of support vector machines (SVMs), a major machine learning model for
classification and regression tasks. Following a recent stream of works on
formal robustness verification of (deep) neural networks, our approach relies
on a sound abstract version of a given SVM classifier to be used for checking
its robustness. This methodology is parametric on a given numerical abstraction
of real values and, analogously to the case of neural networks, needs neither
abstract least upper bounds nor widening operators on this abstraction. The
standard interval domain provides a simple instantiation of our abstraction
technique, which is enhanced with the domain of reduced affine forms, which is
an efficient abstraction of the zonotope abstract domain. This robustness
verification technique has been fully implemented and experimentally evaluated
on SVMs based on linear and nonlinear (polynomial and radial basis function)
kernels, which have been trained on the popular MNIST dataset of images and on
the recent and more challenging Fashion-MNIST dataset. The experimental results
of our prototype SVM robustness verifier appear to be encouraging: this
automated verification is fast, scalable and shows significantly high
percentages of provable robustness on the test set of MNIST, in particular
compared to the analogous provable robustness of neural networks
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