In reinforcement learning for multi-step problems, the sparse nature of the feedback aggravates the difficulty of learning to perform. This paper explores the use of a reinforcement learning architecture, leading to a discussion of reinforcement learning in terms of feature abstraction, credit-assignment, and temporal-difference learning. Issues discussed include: the conditioning of the reinfo...

This paper presents a study of 25 structural features extracted from samples of grapheme ‘th’ that correspond to features commonly used by forensic document examiners. Most of the features are extracted using vector skeletons produced by a specially developed skeletonisation algorithm. The methods of feature extraction are presented along with the results. Analysis of the usefulness of the feat...

Structural vibration monitoring has received a lot of attention from the research community in the past few years. The objective is to create automatic structural assessment techniques that can be realized through programmed vibration analysis. Till now many vibration-based damage features have been proposed, yet to truly automate the damage identification process, reliable damage threshold con...

Abstract. In this paper, we present a methodology for off-line handwritten character recognition. The proposed methodology relies on a new feature extraction technique based on structural characteristics, histograms and profiles. As novelty, we propose the extraction of new eight histograms and four profiles from the 32×32 matrices that represent the characters, creating 256-dimension feature v...

We give asymptotically tight estimate of tangent space variation on a Riemannian submanifold of Euclidean space with respect to the local feature size of the submanifold. We show that the result is a consequence of structural properties of local feature size function of the Riemannian submanifold.

This paper presents a novel feature vector based on physicochemical property of amino acids for prediction protein structural classes. The proposed method is divided into three different stages. First, a discrete time series representation to protein sequences using physicochemical scale is provided. Later on, a wavelet-based time-series technique is proposed for extracting features from mapped...

In this paper, we propose a new approach for keypoint-based object detection. Traditional keypoint-based methods consist in classifying individual points and using pose estimation to discard misclassifications. Since a single point carries no relational features, such methods inherently restrict the usage of structural information to the pose estimation phase. Therefore, the classifier consider...

Remote homology detection refers to the detection of structural homology in proteins when there is little or no sequence similarity. In this article, we present a remote homolog detection method called SVM-HMMSTR that overcomes the reliance on detectable sequence similarity by transforming the sequences into strings of hidden Markov states that represent local folding motif patterns. These stat...

The increasing complexity of the models, the abundant electronic literature and the relative scarcity of the data make it necessary to use the Bayesian approach to complex queries based on prior knowledge and structural models. In the paper we discuss the probabilistic semantics of such statements, the computational challenges and possible solutions of Bayesian inference over complex Bayesian n...