1,721,905 research outputs found
Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification
No full textConventional approaches to training a supervised image classification aim to fully describe all of the classes spectrally. To achieve a complete description of each class in feature space, a large training set is typically required. It is not, however, always necessary to have training statistics that provide a complete and representative description of the classes, especially if using nonparametric classifiers. For classification by a support vector machine, only the training samples that are support vectors, which lie on part of the edge of the class distribution in feature space, are required; all other training samples provide no contribution to the classification analysis. If regions likely to furnish support vectors can be identified in advance of the classification, it may be possible to intelligently select useful training samples. The ability to target useful training samples may allow accurate classification from small training sets. This potential for intelligent training sample collection was explored for the classification of agricultural crops from multispectral satellite sensor data. With a conventional approach to training, only a quarter of the training samples acquired actually made a positive contribution to the analysis and allowed the crops to be classified to a high accuracy (92.5%). The majority of the training set, therefore, was unnecessary as it made no contribution to the analysis. Using ancillary information on soil type, however, it would be possible to constrain the training sample acquisition process. By limiting training sample acquisition only to regions with a specific soil type, it was possible to use a small training set to classify the data without loss of accuracy. Thus, a small number of intelligently selected training samples may be used to classify a data set as accurately as a larger training set derived in a conventional manner. The results illustrate the potential to direct training data acquisition strategies to target the most useful training samples to allow efficient and accurate image classification. <br/
Land cover classification by support vector machine: toward efficient training
No full textThe accuracy of supervised classification is dependent to a large extent on the input training data. In general, the analyst aims to capture a large training set to fully describe the classes spectrally with the conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes if using support vector machine (SVM) as the classifier. A key attraction of the SVM based approach to classification is that it seeks to fit an optimal hyperplane between the classes and since it uses only the training samples that lie at the edge of the class distributions in feature space (support vectors) it may require only a small training sample.The paper shows the potential of SVM of using only a fraction of the training data (support vectors) collected by the usual random scheme for a study carried in the south western part of Punjab state of India
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A relative evaluation of multiclass image classification by support vector machines
Full text linkSupport vector machines (SVMs) have considerable potential as classifiers of remotely sensed data. A constraint on their application in remote sensing has been their binary nature, requiring multiclass classifications to be based upon a large number of binary analyses. Here, an approach for multiclass classification of airborne sensor data by a single SVM analysis is evaluated against a series of classifiers that are widely used in remote sensing, with particular regard to the effect of training set size on classification accuracy. In addition to the SVM, the same datasets were classified using a discriminant analysis, decision tree, and multilayer perceptron neural network. The accuracy statements of the classifications derived from the different classifiers were compared in a statistically rigorous fashion that accommodated for the related nature of the samples used in the analyses. For each classification technique, accuracy was positively related with the size of the training set. In general, the most accurate classifications were derived from the SVM approach, and with the largest training set the SVM classification was significantly (p < 0.05)more accurate (93.75%) than that derived from the discriminant analysis (90.00%) and decision tree algorithms (90.31%). Although each classifier could yield a very accurate classification, > 90% correct, the classifiers differed in the ability to correctly label individual cases and so may be suitable candidates for an ensemble-based approach to classification
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
