74,873 research outputs found
Using the Mean Absolute Percentage Error for Regression Models
We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We show that universal consistency of
Empirical Risk Minimization remains possible using the MAPE instead of the MAE.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015,
Proceedings of the 23-th European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning (ESANN 2015
Reducing offline evaluation bias of collaborative filtering algorithms
Recommendation systems have been integrated into the majority of large online
systems to filter and rank information according to user profiles. It thus
influences the way users interact with the system and, as a consequence, bias
the evaluation of the performance of a recommendation algorithm computed using
historical data (via offline evaluation). This paper presents a new application
of a weighted offline evaluation to reduce this bias for collaborative
filtering algorithms.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium.
pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial
Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015
Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
The latent block model (LBM) is a flexible probabilistic tool to describe
interactions between node sets in bipartite networks, but it does not account
for interactions of time varying intensity between nodes in unknown classes. In
this paper we propose a non stationary temporal extension of the LBM that
clusters simultaneously the two node sets of a bipartite network and constructs
classes of time intervals on which interactions are stationary. The number of
clusters as well as the membership to classes are obtained by maximizing the
exact complete-data integrated likelihood relying on a greedy search approach.
Experiments on simulated and real data are carried out in order to assess the
proposed methodology.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium.
pp.225-230, 2015, Proceedings of the 23-th European Symposium on Artificial
Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Deep neural networks are vulnerable to adversarial examples, i.e.,
carefully-perturbed inputs aimed to mislead classification. This work proposes
a detection method based on combining non-linear dimensionality reduction and
density estimation techniques. Our empirical findings show that the proposed
approach is able to effectively detect adversarial examples crafted by
non-adaptive attackers, i.e., not specifically tuned to bypass the detection
method. Given our promising results, we plan to extend our analysis to adaptive
attackers in future work.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN) 201
Dissimilarity Clustering by Hierarchical Multi-Level Refinement
We introduce in this paper a new way of optimizing the natural extension of
the quantization error using in k-means clustering to dissimilarity data. The
proposed method is based on hierarchical clustering analysis combined with
multi-level heuristic refinement. The method is computationally efficient and
achieves better quantization errors than theComment: 20-th European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012
Modularity-Based Clustering for Network-Constrained Trajectories
We present a novel clustering approach for moving object trajectories that
are constrained by an underlying road network. The approach builds a similarity
graph based on these trajectories then uses modularity-optimization hiearchical
graph clustering to regroup trajectories with similar profiles. Our
experimental study shows the superiority of the proposed approach over classic
hierarchical clustering and gives a brief insight to visualization of the
clustering results.Comment: 20-th European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012
A simple technique for improving multi-class classification with neural networks
We present a novel method to perform multi-class pattern classification with
neural networks and test it on a challenging 3D hand gesture recognition
problem. Our method consists of a standard one-against-all (OAA)
classification, followed by another network layer classifying the resulting
class scores, possibly augmented by the original raw input vector. This allows
the network to disambiguate hard-to-separate classes as the distribution of
class scores carries considerable information as well, and is in fact often
used for assessing the confidence of a decision. We show that by this approach
we are able to significantly boost our results, overall as well as for
particular difficult cases, on the hard 10-class gesture classification task.Comment: European Symposium on artificial neural networks (ESANN), Jun 2015,
Bruges, Belgiu
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