248 research outputs found
Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs
Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we
experiment a set of deep learning models and present a cascaded deep neural
network that can diagnose all 14 pathologies better than the baseline and is
competitive with other published methods. Our work provides the quantitative
results to answer following research questions for the dataset: 1) What loss
functions to use for training DCNN from scratch on ChestX-ray14 dataset that
demonstrates high class imbalance and label co occurrence? 2) How to use
cascading to model label dependency and to improve accuracy of the deep
learning model?Comment: Submitted to CVPR 201
Cascade Training Technique for Particle Identification
The cascade training technique which was developed during our work on the
MiniBooNE particle identification has been found to be a very efficient way to
improve the selection performance, especially when very low background
contamination levels are desired. The detailed description of this technique is
presented here based on the MiniBooNE detector Monte Carlo simulations, using
both artifical neural networks and boosted decision trees as examples.Comment: 12 pages and 4 EPS figure
A multivariate approach to heavy flavour tagging with cascade training
This paper compares the performance of artificial neural networks and boosted
decision trees, with and without cascade training, for tagging b-jets in a
collider experiment. It is shown, using a Monte Carlo simulation of events, that for a b-tagging efficiency of 50%, the light jet
rejection power given by boosted decision trees without cascade training is
about 55% higher than that given by artificial neural networks. The cascade
training technique can improve the performance of boosted decision trees and
artificial neural networks at this b-tagging efficiency level by about 35% and
80% respectively. We conclude that the cascade trained boosted decision trees
method is the most promising technique for tagging heavy flavours at collider
experiments.Comment: 14 pages, 12 figures, revised versio
Combining independent modules to solve multiple-choice synonym and analogy problems
Existing statistical approaches to natural language problems are very
coarse approximations to the true complexity of language processing.
As such, no single technique will be best for all problem instances.
Many researchers are examining ensemble methods that combine the
output of successful, separately developed modules to create more
accurate solutions. This paper examines three merging rules for
combining probability distributions: the well known mixture rule, the
logarithmic rule, and a novel product rule. These rules were applied
with state-of-the-art results to two problems commonly used to assess
human mastery of lexical semantics -- synonym questions and analogy
questions. All three merging rules result in ensembles that are more
accurate than any of their component modules. The differences among the
three rules are not statistically significant, but it is suggestive
that the popular mixture rule is not the best rule for either of the
two problems
Financial Computational Intelligence
Artificial intelligence decision support system is always a popular topic in providing the human with an optimized decision recommendation when operating under uncertainty in complex environments. The particular focus of our discussion is to compare different methods of artificial intelligence decision support systems in the investment domain – the goal of investment decision-making is to select an optimal portfolio that satisfies the investor’s objective, or, in other words, to maximize the investment returns under the constraints given by investors. In this study we apply several artificial intelligence systems like Influence Diagram (a special type of Bayesian network), Decision Tree and Neural Network to get experimental comparison analysis to help users to intelligently select the best portfoliArtificial intelligence, neural network, decision tree, bayesian network
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