Structure Learning in Sequential Data Liam Stewart Master of Science Graduate Department of Computer Science University of Toronto 2005 The goal of discriminative sequence learning is to learn how to classify items that can be arranged in a sequence. Many models have been proposed including logistic regression, the maximum entropy Markov model, the conditional random field, the input output Mar...

We present a data structure that allows to preprocess a rectilinear polygon with n vertices such that, for any two query points, the shortest path in the rectilinear link or L 1-metric can be reported in time O(log n + k) where k is the link length of the shortest path. If only the distance is of interest, the query time reduces to O(log n). Furthermore, if the query points are two vertices, th...

abstract This paper gives a data structure (UDS) for supporting database retrieval, inference and machine learning that attempts to unify and extend previous work in relational databases, semantic networks, conceptual graphs, RETE, neural networks and case-based reasoning. Foundational to this view is that all data can be viewed as a primitive set of objects and mathematical relations (as sets ...

Abstract In this paper we describe I-Subdue, an extension to the Subdue graph-based data mining system. I-Subdue operates over sequentially received relational data to incrementally discover the most representative substructures. The ability to incrementally refine discoveries from serially acquired data is important for many applications, particularly as computer systems become more integrated...

We present a refinement type-based approach for the static verification of complex data structure invariants. Our approach is based on the observation that complex data structures are often fashioned from two elements: recursion (e.g., lists and trees), and maps (e.g., arrays and hash tables). We introduce two novel type-based mechanisms targeted towards these elements: recursive refinements an...

the basic goal of this research is recognition of losses of organizational structure of iranian central insurance and secondary importance are recognition of losses of organizationalcomplexity, organizational formalization and organizational centralization. for data collecting is designed aquestionnaire that is measuring organizational structure situation in robin’s structural dimension’s model...

Concurrent data structures are often used with large concurrent software. An iterator that traverses the data structure items is a highly desirable interface that often exists for sequential data structures but is missing from (almost all) concurrent data-structure implementations. In this paper we introduce a technique for adding a linearizable wait-free iterator to a wait-free or a lock-free ...