Search results for "kernel"
showing 10 items of 357 documents
Informational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithms
2018
Abstract Motivation Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e. how many times each k-mer in {A,C,G,T}k occurs in a DNA sequence. Although this problem is computationally very simple and efficiently solvable on a conventional computer, the sheer amount of data available now in applications demands to resort to parallel and distributed computing. Indeed, those type of algorithms have been developed to collect k-mer statistics in…
A Novel Time Series Kernel for Sequences Generated by LTI Systems
2017
The recent introduction of Hankelets to describe time series relies on the assumption that the time series has been generated by a vector autoregressive model (VAR) of order p. The success of Hankelet-based time series representations prevalently in nearest neighbor classifiers poses questions about if and how this representation can be used in kernel machines without the usual adoption of mid-level representations (such as codebook-based representations). It is also of interest to investigate how this representation relates to probabilistic approaches for time series modeling, and which characteristics of the VAR model a Hankelet can capture. This paper aims at filling these gaps by: deriv…
Residual 𝑝 properties of mapping class groups and surface groups
2008
Let M ( Σ , P ) \mathcal {M}(\Sigma , \mathcal {P}) be the mapping class group of a punctured oriented surface ( Σ , P ) (\Sigma ,\mathcal {P}) (where P \mathcal {P} may be empty), and let T p ( Σ , P ) \mathcal {T}_p(\Sigma ,\mathcal {P}) be the kernel of the action of M ( Σ , P ) \mathcal {M} (\Sigma , \mathcal {P}) on H 1 ( Σ ∖ P , F p ) H_1(\Sigma \setminus \mathcal {P}, \mathbb {F}_p) . We prove that T p ( Σ , P ) \mathcal {T}_p( \Sigma ,\mathcal {P}) is residually p p . In particular, this shows that M ( Σ , P ) \mathcal {M} (\Sigma ,\mathcal {P}) is virtually residually p p . For a group G G we denote by I p ( G ) \mathcal {I}_p(G) the kernel of the natural action of Out ( G ) \ope…
Identification of differential risk hotspots for collision and vehicle type in a directed linear network
2019
Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved. In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014-2017. A directed spatial linear network representing the non-pedestrian ro…
Model selection based product kernel learning for regression on graphs
2013
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the…
Pattern classification using a new border identification paradigm: The nearest border technique
2015
Abstract There are many paradigms for pattern classification such as the optimal Bayesian, kernel-based methods, inter-class border identification schemes, nearest neighbor methods, nearest centroid methods, among others. As opposed to these, this paper pioneers a new paradigm, which we shall refer to as the nearest border (NB) paradigm. The philosophy for developing such a NB strategy is as follows: given the training data set for each class, we shall attempt to create borders for each individual class. However, unlike the traditional border identification (BI) methods, we do not undertake this by using inter-class criteria; rather, we attempt to obtain the border for a specific class in t…
An ensemble analysis of electromyographic activity during whole body pointing with the use of support vector machines.
2011
Import JabRef | WosArea Life Sciences and Biomedicine - Other Topics; International audience; We explored the use of support vector machines (SVM) in order to analyze the ensemble activities of 24 postural and focal muscles recorded during a whole body pointing task. Because of the large number of variables involved in motor control studies, such multivariate methods have much to offer over the standard univariate techniques that are currently employed in the field to detect modifications. The SVM was used to uncover the principle differences underlying several variations of the task. Five variants of the task were used. An unconstrained reaching, two constrained at the focal level and two …
Passive millimeter wave image classification with large scale Gaussian processes
2017
Passive Millimeter Wave Images (PMMWIs) are being increasingly used to identify and localize objects concealed under clothing. Taking into account the quality of these images and the unknown position, shape, and size of the hidden objects, large data sets are required to build successful classification/detection systems. Kernel methods, in particular Gaussian Processes (GPs), are sound, flexible, and popular techniques to address supervised learning problems. Unfortunately, their computational cost is known to be prohibitive for large scale applications. In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed…
Complete One-Loop Renormalization of the Higgs-Electroweak Chiral Lagrangian
2018
The electroweak sector of the Standard Model can be formulated in a way similar to Chiral Perturbation Theory (ChPT), but extended by a singlet scalar. The resulting effective field theory (EFT) is called Higgs-Electroweak Chiral Lagrangian (EWCh$\mathcal{L}$) and is the most general approach to new physics in the Higgs sector. It solely assumes the pattern of symmetry breaking leading to the three electroweak Goldstone bosons (i.e. massive $W$ and $Z$) and the existence of a Higgs-like scalar particle. The power counting of the EWCh$\mathcal{L}$ is given by a generalization of the momentum expansion of ChPT. It is connected to a loop expansion, making the theory renormalizable order by ord…
Machine Learning Methods for Spatial and Temporal Parameter Estimation
2020
Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…