The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Martin V Butz - One of the best experts on this subject based on the ideXlab platform.
-
kernel based ellipsoidal conditions in the real valued xcs classifier system
Genetic and Evolutionary Computation Conference, 2005Co-Authors: Martin V ButzAbstract:Many learning classifier system (LCS) implementations are restricted to the binary problem realm. Recently, the XCS classifier system was enhanced to be able to handle real-valued inputs among others. In the real-valued enhancement, XCSF applies as a function approximation system that partitions the input space in hyperrectangular subspaces specified in the classifiers. This paper changes the classifier conditions to hyperspheres and Hyperellipsoids and investigates the consequent performance impact. It is shown that the modifications yield improved performance in continuous functions. Even in discontinuous functions with parallel boundaries, XCS's performance does not degrade. Thus, for the real-valued problem domain, ellipsoidal condition structures can improve XCS's performance. From a more general perspective, this paper shows that XCS is readily applicable in diverse problem domains. To apply the system even more successfully, suitable kernel-based bases need to be found and used as classifier conditions. XCS distributes the available structures over the problem space evolving more specialized structures in more complex problem subspaces.
-
GECCO - Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05, 2005Co-Authors: Martin V ButzAbstract:Many learning classifier system (LCS) implementations are restricted to the binary problem realm. Recently, the XCS classifier system was enhanced to be able to handle real-valued inputs among others. In the real-valued enhancement, XCSF applies as a function approximation system that partitions the input space in hyperrectangular subspaces specified in the classifiers. This paper changes the classifier conditions to hyperspheres and Hyperellipsoids and investigates the consequent performance impact. It is shown that the modifications yield improved performance in continuous functions. Even in discontinuous functions with parallel boundaries, XCS's performance does not degrade. Thus, for the real-valued problem domain, ellipsoidal condition structures can improve XCS's performance. From a more general perspective, this paper shows that XCS is readily applicable in diverse problem domains. To apply the system even more successfully, suitable kernel-based bases need to be found and used as classifier conditions. XCS distributes the available structures over the problem space evolving more specialized structures in more complex problem subspaces.
Xiaowen Wang - One of the best experts on this subject based on the ideXlab platform.
-
fpga implementation of the projection based recurrent neural network approach to compute the distance between a point and an ellipsoid
International Conference on Neural Information Processing, 2017Co-Authors: Xiaowen WangAbstract:In this paper, an FPGA hardware implementation based on a recurrent neural network was proposed to compute the distance between a point and an ellipsoid. This implementation takes the 0–1 constraint box into consideration as well, it is also capable to solve the Hyperellipsoid problem based on the methodology of an automatic generation of neural hardware tool. The hardware design is based on the Xilinx’s System Generator development tool and experimental results show that the proposed hardware implementation method is very efficient with a high degree of parallelism.
-
ICONIP (6) - FPGA Implementation of the Projection Based Recurrent Neural Network Approach to Compute the Distance Between a Point and an Ellipsoid
Neural Information Processing, 2017Co-Authors: Xiaowen WangAbstract:In this paper, an FPGA hardware implementation based on a recurrent neural network was proposed to compute the distance between a point and an ellipsoid. This implementation takes the 0–1 constraint box into consideration as well, it is also capable to solve the Hyperellipsoid problem based on the methodology of an automatic generation of neural hardware tool. The hardware design is based on the Xilinx’s System Generator development tool and experimental results show that the proposed hardware implementation method is very efficient with a high degree of parallelism.
Marimuthu Palaniswami - One of the best experts on this subject based on the ideXlab platform.
-
ICC - CESVM: Centered Hyperellipsoidal Support Vector Machine Based Anomaly Detection
2008 IEEE International Conference on Communications, 2008Co-Authors: Sutharshan Rajasegarar, Christopher Leckie, Marimuthu PalaniswamiAbstract:A challenge in using machine learning for tasks such as network intrusion detection and fault diagnosis is the difficulty in obtaining clean data for training in order to model the normal behavior of the system. Unsupervised anomaly detection techniques such as one class support vector machines (SVMs) have been introduced to overcome this difficulty. One class support vector machines model the normal or target data using non-linear surfaces in the input space while ignoring the anomalous data. Our approach to this problem is based on fitting a Hyperellipsoid with a minimal effective radius, centered at the origin, around a majority of the data vectors in a higher dimensional space. We formulate this as a linear optimisation problem, which is advantageous in terms of its computational complexity. We demonstrate using real data from the great duck Island Project that our approach achieves better detection performance and flexibility in terms of parameter selection, compared to an earlier detection scheme using a quarter sphere SVM.
G.n. Saridis - One of the best experts on this subject based on the ideXlab platform.
-
Reliability analysis of discrete robotic control systems
[1992] Proceedings of the 31st IEEE Conference on Decision and Control, 1Co-Authors: J.e. Mcinroy, G.n. SaridisAbstract:To automate the process of designing robotic control and sensing systems, techniques are developed for calculating the reliability that the system error stays inside a sequence of Hyperellipsoids. The reliability is then used to select a reliable combination of control and sensing algorithms. An easily calculated lower bound on the liability for multidimensional systems is found. These techniques are then applied to a robotic visual positioning case study which has constraints on the total execution time and the positioning accuracy. Simulation results using PUMA 560 kinematic and dynamic parameters are presented. From 100 possible plans, three are deemed to be reliable in meeting both constraints. >
Alper T. Erdogan - One of the best experts on this subject based on the ideXlab platform.
-
An Algorithmic Framework for Sparse Bounded Component Analysis
IEEE Transactions on Signal Processing, 2018Co-Authors: Eren Babatas, Alper T. ErdoganAbstract:Bounded component analysis (BCA) is a recent approach that enables the separation of both dependent and independent signals from their mixtures. This paper introduces a novel deterministic instantaneous BCA framework for the separation of sparse bounded sources. The framework is based on a geometric maximization setting, where the objective function is defined as the volume ratio of two objects, namely, the principal Hyperellipsoid and the bounding $\ell _1$ -norm ball, defined over the separator output samples. It is shown that all global maxima of this objective are perfect separators. This paper also provides the corresponding iterative algorithms for both real and complex sparse sources. The numerical experiments illustrate the potential benefits of the proposed approach, with applications on image separation and neuron identification.
-
MLSP - Sparse bounded component analysis
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016Co-Authors: Eren Babatas, Alper T. ErdoganAbstract:Bounded Component Analysis (BCA) is a recent approach which enables the separation of both dependent and independent signals from their mixtures. This article introduces a novel deterministic instantaneous BCA approach for the separation of sparse bounded sources. The separation problem is posed as a geometric maximization problem, where the objective is the volume ratio of two geometric objects related to the separator output samples, namely the principal Hyperellipsoid and bounding l 1 norm ball. The global maxima of the corresponding objective are proven to be perfect separators. The article also provides an iterative algorithm corresponding to this objective. The numerical experiments illustrate the potential benefit of the proposed approach relative to existing algorithms.