The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with a sparse kernel representation. However, it does suffer from a number of disadvantages, notably the absence of probabilistic outputs, the requirement to estimate a trade-off parameter and the need to utilise 'Mercer' kernel functions. In this pap...

Support vector machines have attracted much attention in theoretical and in applied statistics. Main topics of recent interest are consistency, learning rates and robustness. In this article, it is shown that support vector machines are qualitatively robust. Since support vector machines can be represented by a functional on the set of all probability measures, qualitative robustness is proven ...

Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. However, an inherent shortage of twin support vector machines is that the resultant hyperplanes are very sensitive to outliers in data. In this paper, we propose centroid tw...

A relevance feedback (RF) approach for content based image retrieval (CBIR) is proposed, which combines Support Vector Machines (SVMs) with Gaussian Mixture (GM) models. Specifically, it constructs GM models of the image features distribution to describe the image content and trains an SVM classifier to distinguish between the relevant and irrelevant images according to the preferences of the u...

The Relevance Vector Machine (RVM) is a recently developed machine learning framework capable of building simple models from large sets of candidate features. Here, we describe a protocol for using the RVM to explore very large numbers of candidate features, and a family of models which apply the power of the RVM to classifying and detecting interesting points and regions in biological sequence...

In this paper, a relevance feedback scheme for both keyword and visual feature-based image retrieval is proposed. For each keyword, a statistical model is trained offline based on visual features of a small set of manually labeled images and used to propagate the keyword to other unlabeled ones. Besides the offline model, another model is constructed online using the user provided positive and ...

A new relevance feedback (RF) approach for content-based image retrieval (CBIR) is presented, which uses Gaussian mixture (GM) models as image representations. The GM of each image is obtained as an adaptation of a universal GM which models the probability distribution of the features of the image database. In each RF round, the positive and negative examples provided by the user until the curr...

The selection of relevant features plays a critical role in relevance feedback for content-based image retrieval. In this paper, we propose an approach for dynamically selecting the most relevant feature space in relevance feedback. During the feedback process, an SVM classifier is constructed in each feature space, and its generalization error is estimated. The feature space with the smallest ...