2,394 research outputs found
Learning in Single Hidden Layer Feedforward Network Models: Backpropagation in a Real World Application
Leaming in neural networks has attracted considerable interest in recent years. Our focus is
on learning in single hidden layer feedforward networks which is posed as a search in the
network parameter space for a network that minimizes an additive error function of
statistically independent examples. In this contribution, we review first the class of single
hidden layer feedforward networks and characterize the learning process in such networks
from a statistical point of view. Then we describe the backpropagation procedure, the leading
case of gradient descent learning algorithms for the class of networks considered here, as
well as an efficient heuristic modification. Finally, we analyse the applicability of these
learning methods to the problem of predicting interregional telecommunication flows.
Particular emphasis is laid on the engineering judgment, first, in choosing appropriate
values for the tunable parameters, second, on the decision whether to train the network by
epoch or by pattern (random approximation), and, third, on the overfitting problem. In
addition, the analysis shows that the neural network model whether using either epoch-based
or pattern-based stochastic approximation outperforms the classical regression approach to
modelling telecommunication flows. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
In this paper, we present a black-box attack against API call based machine
learning malware classifiers, focusing on generating adversarial sequences
combining API calls and static features (e.g., printable strings) that will be
misclassified by the classifier without affecting the malware functionality. We
show that this attack is effective against many classifiers due to the
transferability principle between RNN variants, feed forward DNNs, and
traditional machine learning classifiers such as SVM. We also implement GADGET,
a software framework to convert any malware binary to a binary undetected by
malware classifiers, using the proposed attack, without access to the malware
source code.Comment: Accepted as a conference paper at RAID 201
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia
Optimizing network robustness via Krylov subspaces
We consider the problem of attaining either the maximal increase or reduction
of the robustness of a complex network by means of a bounded modification of a
subset of the edge weights. We propose two novel strategies combining Krylov
subspace approximations with a greedy scheme and an interior point method
employing either the Hessian or its approximation computed via the
limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS). The paper
discusses the computational and modeling aspects of our methodology and
illustrates the various optimization problems on networks that can be addressed
within the proposed framework. Finally, in the numerical experiments we compare
the performances of our algorithms with state-of-the-art techniques on
synthetic and real-world networks
A Mobility Model for Synthetic Travel Demand from Sparse Traces
Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these data suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. The model is tested on sparse mobility traces from Twitter. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and Sa\uf5 Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1-40 km). Moreover, the learned model parameters are found to be transferable from one region to another. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average)
Performance Evaluation of Pathfinding Algorithms
Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls
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