by information systems researchers, and what constitutes valid knowledge about those phenomena. We believe that a single research perspective for studying information systems phenomena is unnecessarily restrictive, and argue that there exist other philosophical assumptions that can inform studies

In the analysis of data it is often assumed that observations y,, y,,...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal

. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show

is to introduce constraints — either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, here we aim for general tracking without

and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signal---that it is a single process having a small number of states

restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly

assumptions now standard within the Government-Binding (GB) framework. The central empirical problem addressed is the question of the proper analysis of the so-called "Poss-ing" gerund in English. This construction possesses simultaneously many properties of sentences, and many properties of noun

is restricted so as to improve discrimination between words in different categories. AND SEIBI CHIBA vestigations were made, based on the assumption that speech patterns are time-sampled with a common and uniform sampling period, as in most general cases. One of the problems discussed in this paper involves

. For efficient search-~Sdefines a most general context in which C holds. A traditional ATMS/TMS is a restricted CMS in which (1) the clauses transmitted to the CMS are limited to be either Horn (i.e., justifications) or negative (i.e., nogoods),

that the problem of finding the ordering that agrees best with a preference function is NP-complete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an on-line learning algorithm, based on the "