in kernelbased machines, for example, perceptrons or support vector machines. In particular, we define different kinds of tree kernels as general approaches to feature engineering in SRL. Moreover, we extensively experiment with such kernels to investigate their contribution to individual stages of an SRL

: This article presents novel methods for classifying glycans and detecting discriminative glycan motifs with support vector machines (SVM). We propose a new class of tree kernels to measure the similarity between glycans. These kernels are based on the comparison of tree substructures, and take into account

representations suitable for machine learning algorithms from linguistic structures is typically difficult. In this paper, we describe one of the most advanced piece of technology for automatic engineering of syntactic and se-mantic patterns. This method merges together convolution depen-dency tree kernels

Detection and Characterization (ACE RDC) corpora shows that the proposed tree kernel-based method outperforms other state-of-the-art methods. rtant ents Mes Much research has been performed on the extraction of semantic relations between named entities. Feature vector-based methods [8

of most effective methods. Many of SVM-based methods focus on finding useful representations of protein sequence, using either explicit feature vector representations or kernel functions. Such representations may suffer from the peaking phenomenon in many machinelearning methods because the features

and classification components. The segmentation assessment includes K-means based agglomerative segmentation and watershed-based segmentation. The classification assessment includes three supervised learning algorithms: Naive Bayes, Bagging with decision trees, and Support Vector Machines (SVM); segments

on advanced type-theoretic machinery that is not easily integrated into established theorem provers. We present Mtac, a lightweight but powerful extension to Coq that supports dependently typed tactic programming. Mtac tactics have access to all the features of ordinary Coq programming, as well as a new set

algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them. Availability and Requirements

In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree

This thesis presents CraneTracker, a multi-modal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable reliable, low-cost monitoring of migratory species. The developed multi-tier architecture yields ecologists with unconventional behavior in-formation not furnished by alternative tracking systems at such a large scale and for a low-cost. The simulation, development and implementation of the CraneTracker soft-ware system is presented. The system is shown effective through multiple proxy deploy-