and relevance models. We carried out experiments to compare time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.

classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important

We examine the long-run implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs, and that both have a weighted universal consistency property. Using the limited memory model we are able to produce learning

propose a descriptive type system for an imperative call-by-value lambda calculus with objects. The calculus models essential features of JavaScript, a widely used dynamically-typed language: first-class functions, objects as property maps, and prototypes. Our type system infers precise singleton object

The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana’s (2002a) temporal context model and M. Usher and J. L. McClelland’s (2001) leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects

) the initially large contribution of a recency model diminishes when the trie is trained on more background training data. 1.

Abstract We provide evidence that primacy and / or recency effects play a crucial role in infant visual categorization. First, we demonstrate that a connectionist model of infant categorization based on a self-organizing map

packet loss and “recency”. A 4 state Markov model is used to represent the time distribution of packet loss during a VoIP call. QoS, Voice over Packet, E model, subjective quality A.

Recency effects (REs) have been well established in memory and probability learning paradigms but have received little attention in category learning research. Extant categorization models predict REs to be unaffected by learning, whereas a functional interpretation of REs, suggested by results

Much evidence indicates that discrimination of the familiarity of visual stimuli is dependent on the perirhinal cortex of the temporal lobe. Within the monkey's perirhinal cortex, &25% of neurons respond strongz to thesig% of novel objects but respond only weakly or brie#y when these objects are seenagnzv These neurons can be divided into three populations based on their patterns of responsiveness. Speci"c temporal dependencies existamong the activities of the three populations of neurons,sugns,z%y the existence of speci"c connections between them. This report concerns computermodelling that indicates how such connections may be used to increase reliability in the determination of whether or not a stimulus isbeing seen for the "rst time. # 2001 Published by Elsevier Science B.V. Keywords Novelty detection; Perirhinal cortex; Hippocampalregpoc Recogampal memory; Familiarity discrimination 1.IM-14 Work in amnesic patients and in monkeys has established that discrimination of ...