Increasingly, organisations flexibly outsource work on a temporary basis to a global audience of workers. This so-called crowdsourcing has been applied successfully to a range of tasks, from translating text and annotating images, to collecting information during crisis situations and hiring skilled workers to build complex software. While traditionally these tasks have been small and could be ...

-In this paper, we introduce a new classification kernel by embedding self organized map (SOM) clustering with mixture of radial basis function (RBF) networks. The model’s efficacy is demonstrated in solving a multi-class TIMIT speech recognition problem where the kernel is used to learn the multidimensional cepstral feature vectors to estimate their posterior class probabilities. The tests res...

For the decision theoretic online (DTOL) setting, we consider methods to construct algorithms that suffer loss not much more than of any sequence of experts distributed along a time interval (shifting experts setting). We present a modified version of the method of Mixing Past Posteriors which uses as basic algorithm AdaHedge with adaptive learning rate. Due to this, we combine the advantages o...

In many computational linguistic scenarios, training labels are subjectives making it necessary to acquire the opinions of multiple annotators/experts, which is referred to as ”wisdom of crowds”. In this paper, we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts (LMDE) model that can automatically learn the prototypical patterns and hidd...

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An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage. This task is fraught with difficulties and uncertainty, making the knowledge provided by human experts essential for successful assessment. Today, the increasing number of progressively complex systems has led to an urgent need to produce tools that support the expe...

This paper presents a simple and effective multiexpert approach based on random subspaces for person reidentification across non-overlapping camera views. This approach applies to supervised learning methods that learn a continuous decision function. Our proposed method trains a group of expert functions, each of which is only exposed to a random subset of the input features. Each expert functi...

We propose a multi-expert restoration scheme to address the model drift problem in online tracking. In the proposed scheme, a tracker and its historical snapshots constitute an expert ensemble, where the best expert is selected to restore the current tracker when needed based on a minimum entropy criterion, so as to correct undesirable model updates. The base tracker in our formulation exploits...

Due to an expected increase in geological voxel model data-flow and user demands, the development of improved quality control for such models is crucial. This study explores the potential of a new type of quality control that improves the detection of errors by just using gaze behavior of 12 geological experts. Gaze is used as input for an attention model that results in attended areas on slice...

We present a new type of multi-class learning algorithm called a linear-max algorithm. Linearmax algorithms learn with a special type of attribute called a sub-expert. A sub-expert is a vector attribute that has a value for each output class. The goal of the multi-class algorithm is to learn a linear function combining the sub-experts and to use this linear function to make correct class predic...