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.
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