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In order to evaluate complex and computationally expensive experiments, data-driven meta-models are used to replace costly experiments and approximate the real experiments' outcome. In this study, an evaluation framework is proposed for measuring the performance of these models. Explored is how the performance is related to the difference between the benchmark optimum and the model optimum, and a...
In this paper, we tackle with indefinite kernels by introducing projection matrix to formulate a positive semidefinite kernel. The projection matrix has a nice property of sharing the same set of eigenvectors with the original kernel. The proposed model can be regarded as a generalized version of spectrum method (denoising method and flipping method) by varying parameter λ. The problem of selecting...
Indoor tracking of smartphones adds context to smartphone applications, enabling a range of smarter behaviours. The predicted use cases are many and varied, and include navigation, planning, advertising and communication. Potentially, indoor tracking could become as ubiquitous as GPS — however, all of these possibilities depend on being able to produce a reasonably accurate, reliable system which...
The method, which is interested in controlling the stability of image invariant features at every stage, is proposed to extract and select new combined invariants for training classifier when aircraft types are recognized. First, a typical aircraft automatic recognition system based on images is analyzed. Second, Hu's moments, Affine moments, Normalized Moment of Inertia and Normalized Fourier Descriptors...
Anomaly diagnostics and fault classification with prognostics is an active research topic, and real-time detection of anomalies and their classification has remained a critical challenge to be overcome. We developed an innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems to mitigate catastrophic failures. The efficacy of the Model-based...
This work proposes a wafer probe parametric test set optimization method for predicting dies which are likely to fail in the field based on known in-field or final test fails. Large volumes of wafer probe data across 5 lots and hundreds of parametric measurements are optimized to find test sets that help predict actually observed test escapes and final test failures. Simple rules are generated to...
The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the framework...
The goal of this research is to find how dependencies affect the capability of several feature selection approaches to extract of the relevant features for a classification purpose. The hypothesis is that more dependencies and higher level dependencies mean more complexity for the task. Some experiments are used to intend to discover some limitations of several feature selection approaches by altering...
Network intrusion detection system needs to handle huge data selected from network environments which usually contain lots of irrelevant or redundant features. It makes intrusion detection with high resource consumption, as well as results in poor performance of real-time processing and intrusion detection rate. Without loss of generality, feature selection can effectively improve the classification...
Support vector machine (SV machine, SVM) is a genius invention with many merits, such as the non-existence of local minima, the largest separating margins of different clusters, as well as the solid theoretical foundation. However, it is also well-noted that SVMs are frequently with a large number of SVs. In this paper, we investigate the number of SVs in a benchmark problem, the parity problem experimentally...
This paper presents a training method of log-linear model for statistical machine translation based on structural support vector machine. This method is designed to directly optimize parameters with respect to translation quality. By adopting maximum-margin principle of SVM, the MT model can learn from training samples with generalization capability. Experiments are carried out on a hierarchical phrase-based...
This paper studies the identification algorithm of parameters self adaptive SMO based on linear kernel function, and analyses its performance and advantages. For ARX model and long-term prediction model, the method is used to identify the model of main steam pressure of thermal system and dual-lane gas turbine engine of aero system. The simulation results show that the algorithm can effectively identify...
This paper focuses on the identification of nonlinear hybrid systems involving unknown nonlinear dynamics. The proposed method extends the framework of by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. In comparison to the previous work of, which also dealt with unknown nonlinearities, the new algorithm assumes the...
A multi-layer adaptive optimizing parameters algorithm is developed for improving least squares support vector machines (LS-SVM), and a military equipment intelligent cost estimation model is proposed based on the optimized LS-SVM. The intelligent cost estimation process is divided into three steps in the model. In the first step, cost-drive-factor is needed to be selected, which is significant for...
Microarchitectural design involves exploring an exponentially large design space in order to determine an optimal configuration for a number of hardware parameters. Determining a particular combination of these parameters which lead to low power consumption can be daunting. New configurations must be tested on software simulators using benchmark programs which typically take a considerable amount...
By considering the geometric properties of the Support Vector Machine (SVM) and Minimal Enclosing Ball (MEB) optimization problems, we show that upper and lower bounds on the radius-margin ratio of an SVM can be efficiently computed at any point during training. We use these bounds to accelerate radius-margin parameter selection by terminating training routines as early as possible, while still obtaining...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
Support vector machines (SVMs) are invaluable tools for many practical applications in artificial intelligence, e.g., classification and event recognition. However, popular SVM solvers are not sufficiently efficient for applications with a great deal of samples as well as a large number of features. In this paper, thus, we present NESVM, a fast gradient SVM solver that can optimize various SVM models,...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
Rare categories abound and their characterization has heretofore received little attention. Fraudulent banking transactions, network intrusions, and rare diseases are examples of rare classes whose detection and characterization are of high value. However, accurate characterization is challenging due to high-skewness and non-separability from majority classes, e.g., fraudulent transactions masquerade...
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