Search results for "Bedding"

Spectral clustering with the probabilistic cluster kernel

2015

Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets.

Fuzzy sigmoid kernel for support vector classifiers

2004

This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.

Feature extraction from remote sensing data using Kernel Orthonormalized PLS

2007

This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.

A novel method for network intrusion detection based on nonlinear SNE and SVM

2017

In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…

Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis

2014

This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…

A family of kernel anomaly change detectors

2014

This paper introduces the nonlinear extension of the anomaly change detection algorithms in [1] based on the theory of reproducing kernels. The presented methods generalize their linear counterparts, under both the Gaussian and elliptically-contoured assumptions, and produce both improved detection accuracies and reduced false alarm rates. We study the Gaussianity of the data in Hilbert spaces with kernel dependence estimates, provide low-rank kernel versions to cope with the high computational cost of the methods, and give prescriptions about the selection of the kernel functions and their parameters. We illustrate the performance of the introduced kernel methods in both pervasive and anom…

Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel

2008

This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.

Revisiting the role of embedding in Systemic Functional Linguistics : Construing depth in “big texts”

2021

This paper is concerned with exploring the role of embedded genres in expanding the meaning potential of long texts, focusing especially on complex tertiary assignments. A significant body of work has evolved in Systemic Functional Linguistics (SFL) on modelling “big texts” as macrogenres since Martin (1994: 29) posed the question how texts “get bigger than a page”. However, based on detailed genre analyses of high-scoring undergraduate business country reports, this paper illustrates that not all big texts are macrogenres made up of elemental genre complexes. Analogising from clause grammar, it will show that these texts unfold through multiple layers of embedded genres in their generic st…

The historical role played by the work of Ulisse Dini on implicit function theory in the modern differential geometry foundations: the case of the st…

2012

The Group Mind Hypothesis between Social ontology and Philosophy of Mind: Some Critical Notes

2013

Some recent theoretical analyses of collective behavior in social ontology, philosophy of mind and situated cognitive science have proposed arguments to revive the notion of group mind as the proper bearer of joint cognitive processes and actions. In this paper I analyse two kinds of arguments supporting this view: first, since group reasons in joint actions claim, at least sometimes, to have a priority over individual reasons, then groups are psychologically autonomous from their members. Second, the structure of the causal and functional dynamics of individual and collective cognition mirror each other in such a way that, by parity of reasoning, we must talk of a collective mind as underl…