We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. The algorithm's predictive accuracy is competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.

This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear kernel regression methods: namely kernel ridge regression (KRR) and relevance vector regression (RVR). RVR is based on a Bayesian framework, which automatically determines a sparse solution through maximization of marginal likelihood. KRR is the dual-form formulation of ridge regression, which solves regression problems with high dimensional data in a computationally efficient way. Feature selection based on prior knowledge about human brain function was also used. Post-processing by constrained deconvolution and re-convolution was used to furnish the prediction. This paper also contains a detailed description of how prior knowledge was used to fine tune predictions of specific “feature ratings,” which we believe is one of the key factors in our prediction accuracy. The impact of pre-processing was also evaluated, demonstrating that different pre-processing may lead to significantly different accuracies. Although the original work was aimed at the PBAIC, many techniques described in this paper can be generally applied to any fMRI decoding works to increase the prediction accuracy.

When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these confidence values have no theoretical base-even though the algorithms' predictive performance may be good. There also exist many successful learning algorithms which only depend on the iid assumption. Often however they produce no confidence values for their predictions. Bayesian frameworks are often applied to these algorithms in order to obtain such values, however they can rely on unjustified priors. In this paper we outline the typicalness framework which can be used in conjunction with many other machine learning algorithms. The framework provides confidence information based only on the standard iid assumption and so is much more robust to different underlying data distributions. We show how the framework can be applied to existing algorithms. We also present experimental results which show that the typicalness approach performs close to Bayes when the prior is known to be correct. Unlike Bayes however, the method still gives accurate confidence values even when different data distributions are considered.

Query formulation and efficient navigation through data to reach relevant results are undoubtedly major challenges for image or video retrieval. Queries of good quality are typically not available and the search process needs to rely on relevance feedback given by the user, which makes the search process iterative. Giving explicit relevance feedback is laborious, not always easy, and may even be impossible in ubiquitous computing scenarios. A central question then is: Is it possible to replace or complement scarce explicit feedback with implicit feedback inferred from various sensors not specifically designed for the task? In this paper, we present preliminary results on inferring the relevance of images based on implicit feedback about users' attention, measured using an eye tracking device. It is shown that, in reasonably controlled setups at least, already fairly simple features and classifiers are capable of detecting the relevance based on eye movements alone, without using any explicit feedback

In this paper 1 we propose a new algorithm for providing confidence and credibility values for predictions on a multi-class pattern recognition problem which uses Support Vector machines in its implementation. Previous algorithms which have been proposed to achieve this are very processing intensive and are only practical for small data sets. We present here a method which overcomes these limitations and can deal with larger data sets (such as the US Postal Service database). The measures of confidence and credibility given by the algorithm are shown empirically to reflect the quality of the predictions obtained by the algorithm, and are comparable to those given by the less computationally efficient method. In addition to this the overall performance of the algorithm is shown to be comparable to other techniques (such as standard Support Vector machines), which simply give flat predictions and do not provide the extra confidence/credibility measures.

Abstract. The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformations. The resulting descriptors are utilised as visual terms for each image. We first aim to annotate query images by retrieving images that are similar to the query image. This approach uses the analogy that similar images would be annotated similarly as well. We then propose an image annotation method that learns a direct mapping from image descriptors to keywords. We compare the semantic based methods of Latent Semantic Indexing and Kernel Canonical Correlation Analysis (KCCA), as well as using a recently proposed vector label based learning method known as Maximum Margin Robot.

Introduction Interruptions in treatment pose risks for people with HIV (PWH) and threaten progress in ending the HIV epidemic; however, the COVID‐19 pandemic's impact on HIV service delivery across diverse settings is not broadly documented. Methods From September 2020 to March 2021, the International epidemiology Databases to Evaluate AIDS (IeDEA) research consortium surveyed 238 HIV care sites across seven geographic regions to document constraints in HIV service delivery during the first year of the pandemic and strategies for ensuring care continuity for PWH. Descriptive statistics were stratified by national HIV prevalence (<1%, 1–4.9% and ≥5%) and country income levels. Results Questions about pandemic‐related consequences for HIV care were completed by 225 (95%) sites in 42 countries with low ( n = 82), medium ( n = 86) and high ( n = 57) HIV prevalence, including low‐ ( n = 57), lower‐middle ( n = 79), upper‐middle ( n = 39) and high‐ ( n = 50) income countries. Most sites reported being subject to pandemic‐related restrictions on travel, service provision or other operations (75%), and experiencing negative impacts (76%) on clinic operations, including decreased hours/days, reduced provider availability, clinic reconfiguration for COVID‐19 services, record‐keeping interruptions and suspension of partner support. Almost all sites in low‐prevalence and high‐income countries reported increased use of telemedicine (85% and 100%, respectively), compared with less than half of sites in high‐prevalence and lower‐income settings. Few sites in high‐prevalence settings (2%) reported suspending antiretroviral therapy (ART) clinic services, and many reported adopting mitigation strategies to support adherence, including multi‐month dispensing of ART (95%) and designating community ART pick‐up points (44%). While few sites (5%) reported stockouts of first‐line ART regimens, 10–11% reported stockouts of second‐ and third‐line regimens, respectively, primarily in high‐prevalence and lower‐income settings. Interruptions in HIV viral load (VL) testing included suspension of testing (22%), longer turnaround times (41%) and supply/reagent stockouts (22%), but did not differ across settings. Conclusions While many sites in high HIV prevalence settings and lower‐income countries reported introducing or expanding measures to support treatment adherence and continuity of care, the COVID‐19 pandemic resulted in disruptions to VL testing and ART supply chains that may negatively affect the quality of HIV care in these settings.

In this paper, we tackle the problem of associating combinations of colors to abstract categories (e.g. capricious, classic, cool, delicate, etc.). It is evident that such concepts would be difficult to distinguish using single colors, therefore we consider combinations of colors or color palettes. We leverage two novel databases for color palettes and we learn categorization models using low and high level descriptors. Preliminary results show that Fisher representation based on GMMs is the most rewarding strategy in terms of classification performance over a baseline model. We also suggest a process for cleaning weakly annotated data, whilst preserving the visual coherence of categories. Finally, we demonstrate how learning abstract categories on color palettes can be used in the application of color transfer, personalization and image re-ranking.