Zeeshan Khawar Malik

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In this paper, we aim to develop novel learning approaches for extracting invariant features from time series. Specifically, we implement an existing method of solving the generalized eigenproblem and use this to firstly implement the biologically inspired technique of slow feature analysis (SFA) originally developed by Wiskott and Sejnowski (Neural Comput(More)
This paper presents a novel online version of laplacian eigenmap termed as generalized incremental laplacian eigenmap (GENILE), one of the most popular manifold-based dimensionality reduction technique performed by solving the generalized eigenvalue problem. We have used swiss roll and s-curve dataset, the most popular datasets used for manifold-based(More)
As demonstrated earlier, the learning accuracy of the single-layer-feedforward-network (SLFN) is generally far lower than expected, which has been a major bottleneck for many applications. In fact, for some large real problems, it is accepted that after tremendous learning time (within finite epochs), the network output error of SLFN will stop or reduce(More)
In this paper, we present a novel architecture and learning algorithm for a multilayered echo state machine (ML-ESM). Traditional echo state networks (ESNs) refer to a particular type of reservoir computing (RC) architecture. They constitute an effective approach to recurrent neural network (RNN) training, with the (RNN-based) reservoir generated randomly,(More)
—Deep Neural Networks, and specifically fully-connected convolutional neural networks are achieving remarkable results across a wide variety of domains. They have been trained to achieve state-of-the-art performance when applied to problems such as speech recognition, image classification, natural language processing and bioinformatics. Most of these deep(More)
In this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by developing an online version of the CCA and apply it to reservoirs of an echo state network in order to capture(More)
—Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network(More)
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