26 research outputs found
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
The Echo State Network (ESN) is a specific recurrent network, which has
gained popularity during the last years. The model has a recurrent network
named reservoir, that is fixed during the learning process. The reservoir is
used for transforming the input space in a larger space. A fundamental property
that provokes an impact on the model accuracy is the Echo State Property (ESP).
There are two main theoretical results related to the ESP. First, a sufficient
condition for the ESP existence that involves the singular values of the
reservoir matrix. Second, a necessary condition for the ESP. The ESP can be
violated according to the spectral radius value of the reservoir matrix. There
is a theoretical gap between these necessary and sufficient conditions. This
article presents an empirical analysis of the accuracy and the projections of
reservoirs that satisfy this theoretical gap. It gives some insights about the
generation of the reservoir matrix. From previous works, it is already known
that the optimal accuracy is obtained near to the border of stability control
of the dynamics. Then, according to our empirical results, we can see that this
border seems to be closer to the sufficient conditions than to the necessary
conditions of the ESP.Comment: 23 pages, 14 figures, accepted paper for the IEEE IJCNN, 201
Echo State Queueing Network: a new reservoir computing learning tool
In the last decade, a new computational paradigm was introduced in the field
of Machine Learning, under the name of Reservoir Computing (RC). RC models are
neural networks which a recurrent part (the reservoir) that does not
participate in the learning process, and the rest of the system where no
recurrence (no neural circuit) occurs. This approach has grown rapidly due to
its success in solving learning tasks and other computational applications.
Some success was also observed with another recently proposed neural network
designed using Queueing Theory, the Random Neural Network (RandNN). Both
approaches have good properties and identified drawbacks. In this paper, we
propose a new RC model called Echo State Queueing Network (ESQN), where we use
ideas coming from RandNNs for the design of the reservoir. ESQNs consist in
ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The
paper positions ESQNs in the global Machine Learning area, and provides
examples of their use and performances. We show on largely used benchmarks that
ESQNs are very accurate tools, and we illustrate how they compare with standard
ESNs.Comment: Proceedings of the 10th IEEE Consumer Communications and Networking
Conference (CCNC), Las Vegas, USA, 201
Tracking changes using Kullback-Leibler divergence for the continual learning
Recently, continual learning has received a lot of attention. One of the
significant problems is the occurrence of \emph{concept drift}, which consists
of changing probabilistic characteristics of the incoming data. In the case of
the classification task, this phenomenon destabilizes the model's performance
and negatively affects the achieved prediction quality. Most current methods
apply statistical learning and similarity analysis over the raw data. However,
similarity analysis in streaming data remains a complex problem due to time
limitation, non-precise values, fast decision speed, scalability, etc. This
article introduces a novel method for monitoring changes in the probabilistic
distribution of multi-dimensional data streams. As a measure of the rapidity of
changes, we analyze the popular Kullback-Leibler divergence. During the
experimental study, we show how to use this metric to predict the concept drift
occurrence and understand its nature. The obtained results encourage further
work on the proposed methods and its application in the real tasks where the
prediction of the future appearance of concept drift plays a crucial role, such
as predictive maintenance.Comment: Accepted manuscript in SMC 2022, it will be published in the IEEE
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Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
The article presents an application of Hidden Markov Models (HMMs) for
pattern recognition on genome sequences. We apply HMM for identifying genes
encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma
brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa
causative agents of sleeping sickness and several diseases in domestic and wild
animals. These parasites have a peculiar strategy to evade the host's immune
system that consists in periodically changing their predominant cellular
surface protein (VSG). The motivation for using patterns recognition methods to
identify these genes, instead of traditional homology based ones, is that the
levels of sequence identity (amino acid and DNA sequence) amongst these genes
is often below of what is considered reliable in these methods. Among pattern
recognition approaches, HMM are particularly suitable to tackle this problem
because they can handle more naturally the determination of gene edges. We
evaluate the performance of the model using different number of states in the
Markov model, as well as several performance metrics. The model is applied
using public genomic data. Our empirical results show that the VSG genes on T.
brucei can be safely identified (high sensitivity and low rate of false
positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications,
Springer. The article contains 23 pages, 4 figures, 8 tables and 51
reference
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Temporal learning using echo state network for human activity recognition
Several works have been applied to non-temporal classification techniques in the Human Activity Recognition area. Instead of that, we present an approach for modelling human activities using a temporal learning tool. Here, the activities are considered as time-dependent events, and we use a temporal learning method for their classification. We employ a well-known learning tool named Echo State Network (ESN). An ESN is a specific type of Recurrent Neural Networks, which has proven well performances for solving benchmark problems with sequential and time-series data. Another advantage is that the method is very robust and fast during the learning algorithm. Therefore, it is a good tool for being applied in real-time contexts. We apply the proposed approach for analyzing a well-know benchmark dataset, and we obtain promising results
Experimental analysis on dissimilarity metrics and sudden concept drift detection
International audienceLearning from non-stationary data presents several new challenges. Among them, a significant problem comes from the sudden changes in the incoming data distributions, the so-called concept drift. Several concept drift detection methods exist, generally based on distances between distributions, either arbitrarily selected or context-dependent. This paper presents a straightforward approach for detecting concept drift based on a weighted dissimilarity metric over posterior probabilities. We also evaluate the performance of three well-known dissimilarity metrics when used by the proposed approach. Experimental evaluation has been done over ten datasets with injected sudden drifts in a binary classification context. Our results first suggest choosing the Kullback-Leibler divergence, and second, they show that our drift detection procedure based on dissimilarity measures is pretty efficient
An Experimental Analysis of Reservoir Parameters of the Echo State Queueing Network Model
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