of the query and document collection on the optimal settings of retrieval parameters. As a special case, we present a two-stage smoothing method that allows us to estimate the smoothing parameters completely automatically. In the first stage, the document language model is smoothed using a Dirichlet prior

Abstract. Feature models are used to specify members of a product-line. Despite years of progress, contemporary tools provide limited support for feature constraints and offer little or no support for debugging feature models. We integrate prior results to connect feature models, grammars

experiments indicated that this transient attentional component was independent of the observers ’ prior knowledge of target position and was not subject to voluntary control. We provide evidence to suggest hat the transient component does not originate at the earliest stages of visual processing, since

-UCS interval in eyelid conditioning and it is not obvious that the construct so quantified can be readily transferred to verbal learning. One objection is that a verbal stimulus produces a strong predictable response prior to the experimental session and this is not true of the originally neutral stimulus

For the 2 decades prior to 1960, published research in social psychology was based on a wide variety of subjects and research sites. Content analyses show that since then such research has overwhelmingly been based on college students tested in academic laboratories on academiclike tasks. How might

;rigorously" an involved dynamic programming problem must be solved. The approximation techniques proposed for this purpose are largely at a conceptual stage. Among the broader research needs the following areas are identified: multivariable system identification, performance monitoring and diagnostics, nonlinear state

global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps

the conditional reference prior is proposed. A su-cient condition is then given under which this conditional reference prior agrees with the conditional reference prior derived in the rst stage of the reference prior algorithm of Berger and Bernardo. Finally, under the assumption of independence, a method

that argument strength (i) increases with the overlap between features of the combined premise categories and features of the conclusion category; and (ii) decreases with the amount of prior knowledge about the conclusion category. The model assumes a two-stage process. First, premises are encoded by connecting

types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets