298 research outputs found
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems
This research presents an IDS prototype in Matlab that assess network traffic connections contained in the
NSL-KDD dataset, comparing feature selection techniques available in FEAST toolbox, refining prior
results applying dimension reduction technique ISOMAP. The classification process used a supervised
learning technique called Support Vector Machines (SVM). The comparative analysis related to detection
rates by attack category are conclusive that MRMR+PCA+SVM (selection, reduction and classification
techniques) combined obtained more promising results, just using 5 of 41 available features in the dataset.
The results obtained were: 85.42% normal traffic, 80.77% DoS, 90.41% Probe, 91.78% U2R and 83.25%
R2L
Signal processing methods for EEG data classification
Imperial Users onl
Robust Auto-encoders
In this thesis, our aim is to improve deep auto-encoders, an important topic in the deep learning area, which has shown connections to latent feature discovery models in the literature. Our model is inspired by robust principal component analysis, and we build an outlier filter on the top of basic deep auto-encoders. By adding this filter, we can split the input data X into two parts X=L+S, where the L could be better reconstructed by a deep auto-encoder and the S contains the anomalous parts of the original data X. Filtering out the anomalies increases the robustness of the standard auto-encoder, and thus we name our model ``Robust Auto-encoder\u27. We also propose a novel solver for the robust auto-encoder which alternatively optimizes the reconstruction cost of the deep auto-encoder and the sparsity of outlier filter in pursuit of finding the optimal solution. This solver is inspired by the Alternating Direction Method of Multipliers, Back-propagation and the Alternating Projection method, and we demonstrate the convergence properties of this algorithm and its superior performance in standard image recognition tasks. Last but not least, we apply our model to multiple domains, especially, the cyber-data analysis, where deep models are seldom currently used
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work, we propose to leverage the advantage of such features and analyze
key components that lead to better performances. Specifically, we show that a
significant performance gain could be obtained by combining convolutional
architectures with approximate top- ranking objectives, as thye naturally
fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset
outperforms the conventional visual features by about 10%, obtaining the best
reported performance in the literature
A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions
Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.Peer ReviewedPostprint (published version
Classification of Physiological Signals for Emotion Recognition using IoT
Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion
- …