We propose to directly measure the importance of queries in the source domain to the target domain where no rank labels of documents are available, which is referred to as query weighting. Query weighting is a key step in ranking model adaptation. As the learning object of ranking algorithms is divided by query instances, we argue that it’s more reasonable to conduct importance weighting at que...

Most of traditional ear recognition methods that based on local features always need accurate images alignment, which may severely affect the performance. In this paper, we investigate a novel approach for ear recognition based on Polar Sine Transform (PST); PST is free of images alignment. First, we divide the ear images into overlapping blocks. After that, we compute PST coefficients that are...

Support vector machine (SVM) has received much attention in feature selection recently because of its ability to incorporate kernels to discover nonlinear dependencies between features. However it is known that the number of support vectors required in SVM typically grows linearly with the size of the training data set. Such a limitation of SVM becomes more critical when we need to select a sma...

Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the sample format used to learn the model. We also propose an enriched classification based set-up that uses a set of standard distances to supplement the informatio...

Learning a complex task can be significantly facilitated by defining a hierarchy of subtasks. An agent can learn to choose between various temporally abstract actions, each solving an assigned subtask, to accomplish the overall task. In this paper, we study hierarchical learning using the framework of options. We argue that to take full advantage of hierarchical structure, one should perform op...

The paper proposes a novel approach for classification of sports images based on the geometric information encoded in the image of a sport’s field. The proposed approach uses invariant nature of a crossratio under projective transformation to develop a robust classifier. For a given image, cross-ratios are computed for the points obtained from the intersection of lines detected using Hough tran...

The aim of this study is to investigate Fourier Descriptor (FD) as feature vectors for shape representation and recognition since FD is the best known boundary based shape descriptor and has proven to outperform most other boundary based methods in terms of accuracy. Furthermore, FD is also invariant to geometric transformations and has good noise tolerance. The main concern regarding FD is the...

CLaC-CORE, an exhaustive feature combination system ranked 4th among 34 teams in the Semantic Textual Similarity shared task STS 2013. Using a core set of 11 lexical features of the most basic kind, it uses a support vector regressor which uses a combination of these lexical features to train a model for predicting similarity between sentences in a two phase method, which in turn uses all combi...

Meta-features are used to describe properties and characteristics of datasets and construct the feature space for meta-learning. Many of the different meta-features are defined for single variables and, therefore, are computed per feature of the dataset. Since datasets contain different numbers of features but meta-learning requires feature vectors of the same size, such measures are typically ...

We propose an interactive technique that allows the user to visually explore the feature space around relevant images and to focus the search on only those regions in feature space that are relevant. Through the use of a novel interface, the user can adjust the exploration front around each relevant image, visually setting the range within which images are also considered to be relevant. By giv...