External Support Vector Machine Clustering

The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector\u27s class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data\u27s clustering characteristics, or the number of clusters

External Support Vector Machine Clustering

The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector\u27s class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cluster identification upon iterating the overall algorithm on the positive and negative clusters identified (until the clusters are no longer separable into sub-clusters), this method provides a way to cluster data sets without prior knowledge of the data\u27s clustering characteristics, or the number of clusters

What is news? News values revisited (again)

The deceptively simple question “What is news?” remains pertinent even as we ponder the future of journalism in the digital age. This article examines news values within mainstream journalism and considers the extent to which news values may be changing since earlier landmark studies were undertaken. Its starting point is Harcup and O’Neill’s widely-cited 2001 updating of Galtung and Ruge’s influential 1965 taxonomy of news values. Just as that study put Galtung and Ruge’s criteria to the test with an empirical content analysis of published news, this new study explores the extent to which Harcup and O’Neill’s revised list of news values remain relevant given the challenges (and opportunities) faced by journalism today, including the emergence of social media. A review of recent literature contextualises the findings of a fresh content analysis of news values within a range of UK media 15 years on from the last study. The article concludes by suggesting a revised and updated set of contemporary news values, whilst acknowledging that no taxonomy can ever explain everything

Support Vector Machine Implementations for Classification & Clustering

BACKGROUND: We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition. The SVM approach encapsulates a significant amount of model-fitting information in the choice of its kernel. In work thus far, novel, information-theoretic, kernels have been successfully employed for notably better performance over standard kernels. Currently there are two approaches for implementing multiclass SVMs. One is called external multi-class that arranges several binary classifiers as a decision tree such that they perform a single-class decision making function, with each leaf corresponding to a unique class. The second approach, namely internal-multiclass, involves solving a single optimization problem corresponding to the entire data set (with multiple hyperplanes). RESULTS: Each SVM approach encapsulates a significant amount of model-fitting information in its choice of kernel. In work thus far, novel, information-theoretic, kernels were successfully employed for notably better performance over standard kernels. Two SVM approaches to multiclass discrimination are described: (1) internal multiclass (with a single optimization), and (2) external multiclass (using an optimized decision tree). We describe benefits of the internal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy. In situations where the data isn't clearly separable, making for poor discrimination, signal clustering is used to provide robust and useful information – to this end, novel, SVM-based clustering methods are also described. As with the classification, there are Internal and External SVM Clustering algorithms, both of which are briefly described

Money, (Co)Production and Power in Digital

This article discusses the contribution of critical political economy approaches to digital journalism studies and argues that these offer important correctives to celebratory perspectives. The first part offers a review and critique of influential claims arising from self-styled new studies of convergence culture, media and creative industries. The second part discusses the contribution of critical political economy in examining digital journalism and responding to celebrant claims. The final part reflects on problems of restrictive normativity and other limitations within media political economy perspectives and considers ways in which challenges might be addressed by more synthesising approaches. The paper proposes developing radical pluralist, media systems and comparative analysis, and advocates drawing on strengths in both political economy and culturalist traditions to map and evaluate practices across all sectors of digital journalism