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We establish connections between Cohen-Posch (1985) theory of positive time-frequency distributions (TFDs) and copula theory. Both are aimed at designing joint probability distributions with fixed marginals, and we demonstrate that they are formally equivalent. Moreover, we show that copula theory leads to a noniterative method for constructing positive(More)
Dimensionality reduction techniques are commonly used in text categorisation problems to improve training and classification efficiency as well as to avoid overfitting. The best performing dimensionality reduction techniques for text categorisation are supervised, hence utilise the label information of the training data. Active learning is used to reduce(More)
While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial(More)
This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimized in a principled way. Simulations demonstrate that our low-complexity method outperforms state-of-the-art nonstationary signal classification techniques.
Time-frequency representations (TFRs) are efficient tools for nonstationary signal classification. However, the choice of the TFR and of the distance measure employed is critical when no prior information other than a learning set of limited size is available. In this letter, we propose to jointly optimize the TFR and distance measure by minimizing the(More)
In this paper we describe our work in the area of topic-based sentiment analysis in the domain of financial blogs. We explore the use of paragraph-level and document-level annotations, examining how additional information from paragraph-level annotations can be used to increase the accuracy of document-level sentiment classification. We acknowledge the(More)
Active learning with history-based query selection for text categorisation. Active learning with history-based query selection for text categorisation. Active learning with history-based query selection for text categorisation. Abstract. Automated text categorisation systems learn a generalised hypothesis from large numbers of labelled examples. However, in(More)
Error-reduction sampling (ERS) is a high performing (but computationally expensive) query selection strategy for active learning. Subset optimisation has been proposed to reduce computational expense by applying ERS to only a subset of examples from the pool. This paper compares techniques used to construct the subset, namely random sub-sampling and(More)