Search results for " set"
showing 10 items of 2095 documents
Potencialidades de Google Maps en la investigación social aplicada
2019
In recent years, Google has devoted resources to build a complete map of the world. They constantly scan the territory, collecting a large amount of data that provides updated and complete geographic information. This allows us to have an interoperable map that provides the end user with a search tool, not only of routes but also of shops, equipment and any type of geo-referenced information. In addition, Google Maps provides a series of Application Programming Interface (API), which provides a library of set of subroutines, functions and procedures (in object-oriented programming) that can be used by other software to automate the extraction of information of the Google platform. These fre…
Predicting bond betas using macro-finance variables
2019
We conduct in-sample and out-of-sample forecasting using the new approach of combining explanatory variables through complete subset regressions (CSR). We predict bond CAPM betas and bond returns conditioning on various macro-finance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high-yield corporate bonds. The CSR method performs well in predicting bond betas, especially in-sample, and, mainly high-yield bond betas when the focus is out-of-sample. Bond returns are less predictable than bond betas.
Semisupervised nonlinear feature extraction for image classification
2012
Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…
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…
Radio Labelings of Distance Graphs
2013
A radio $k$-labeling of a connected graph $G$ is an assignment $c$ of non negative integers to the vertices of $G$ such that $$|c(x) - c(y)| \geq k+1 - d(x,y),$$ for any two vertices $x$ and $y$, $x\ne y$, where $d(x,y)$ is the distance between $x$ and $y$ in $G$. In this paper, we study radio labelings of distance graphs, i.e., graphs with the set $\Z$ of integers as vertex set and in which two distinct vertices $i, j \in \Z$ are adjacent if and only if $|i - j| \in D$.
COMPARISON OF CRIMSON FOUNTAINGRASS AND DISS FIBRES AS AGGREGATES FOR CEMENT MORTARS
2019
The use of natural fibres in cement composites is an expanding research field as their use can improve the mechanical and thermal behavior of cement mortars and reduce their carbon footprint. In this paper two different wild grasses, i.e. Pennisetum Setaceum, also known as crimson fountaingrass, and Ampelodesmos Mauritanicus, also called diss, are used as source of natural fibres for cement mortars. The principal aim is to evaluate the possibility of using the more invasive crimson fountaingrass in place of diss inside cement based vegetable concrete. The two plants’ fibres have been characterized by means of electron microscopy, helium picnometry; moreover, the thermal conductivity of fibr…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
2011
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…
Analyzing Protein-Protein Spatial-Temporal Dependencies from Image Sequences Using Fuzzy Temporal Random Sets
2008
Total Internal Reflection Fluorescence Microscopy (TIRFM) allows us to image fluorescenttagged proteins near the plasma membrane of living cells with high spatial-temporal resolution. Using TIRFM imaging of GFP-tagged clathrin endocytic proteins, areas of fluorescence are observed as overlapping spots of different sizes and durations. Standard procedures to measure protein-protein colocalization of dual labeled samples threshold the original graylevel images to segment areas covered by different proteins. This binary logic is not appropriate as it leaves a free tuning parameter which can influence the conclusions. Moreover, these procedures rely on simple statistical analysis based on corre…
Domain separation for efficient adaptive active learning
2011
This paper proposes a procedure aimed at efficiently adapting a classifier trained on a source image to a similar target image. The adaptation is carried out through active queries in the target domain following a strategy particularly designed for the case where class distributions have shifted between the two images. We first suggest a pre-selection of candidate pixels issued from the target image by keeping only those samples appearing to be lying in a region of the input space not yet covered by the existing ground truth (source domain pixels). Then, exploiting a classifier integrating instance weights, active queries are performed on the target image. As the inclusion to the training s…
Yield Management and Airline Strategic Groups
2011
A new strategic group of companies has appeared in Europe. This group, low-cost airlines, differs markedly from the traditional flagship companies. Focusing on a dynamic approach, the study discussed in this paper was developed to determine whether companies in different strategic groups adopted different price setting methods depending on which group they belonged to. The study is based on price information recorded at 11 different time periods for 12 airlines.