Monmouth University

About: Monmouth University is a education organization based out in West Long Branch, New Jersey, United States. It is known for research contribution in the topics: Wireless network & Wireless sensor network. The organization has 794 authors who have published 1882 publications receiving 38676 citations.

Support Vector Regression Machines

Abstract: A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.

Consistent query answers in inconsistent databases

Abstract: In this paper we consider the problem of the logical characterization of the notion of consistent answer in a relational database that may violate given integrity constraints. This notion is captured in terms of the possible repaired versions of the database. A method for computing consistent answers is given and its soundness and completeness (for some classes of constraints and queries) proved. The method is based on an iterative procedure whose termination for several classes of constraints is proved as well.

Generic knowledge strategies in the U.S. pharmaceutical industry

Abstract: The purpose of this study is to identify groups of firms with similar generic knowledge strategies, determine how these strategies change over time, and compare profit margins of the groups. Knowledge strategies of 21 U.S. pharmaceutical firms are analyzed from 1977 to 1991. Cluster analysis is used to group firms over different time periods based on: (a) balance between internal and external learning, (b) preference for radical or incremental learning, (c) learning speed, and (d) breadth of knowledge base. Our findings indicate that there are four generic knowledge strategy groups: ‘Explorers’, ‘Exploiters’, ‘Loners’, and ‘Innovators’. Most firms remain in the same knowledge group over time. The firms in the ‘Innovator’ and ‘Explorer’ groups tend to be more profitable than the firms in the ‘Exploiter’ and ‘Loner’ groups.

The Effects of School-Based Writing-to-Learn Interventions on Academic Achievement: A Meta-Analysis

Abstract: Since the early 1970s, many educators have touted writing as a means of enhancing learning. Several reasons have been suggested for this purported enhancement: that writing is a form of learning, that writing approximates human speech, that writing supports learning strategies. Alternatively, some researchers have cautioned that the educative effects of writing may be contingent on the contexts in which it occurs. The research on writing’s effects on learning is ambiguous. This meta-analysis of 48 school-based writing-to-learn programs shows that writing can have a small, positive impact on conventional measures of academic achievement. Two factors predicted enhanced effects: the use of metacognitive prompts and increased treatment length. Two factors predicted reduced effects: implementation in Grades 6–8 and longer writing assignments

iDistance: An adaptive B+-tree based indexing method for nearest neighbor search

Abstract: In this article, we present an efficient Bp-tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a Bp-tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution.We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization.