Results 11 - 20 of 10,099

Toward an instance theory of automatization

by Gordon D. Logan - Psychological Review , 1988
"... This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur ..."
Abstract - Cited by 647 (38 self) - Add to MetaCart
This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur

Testing for Common Trends

by James H. Stock, Mark W. Watson - Journal of the American Statistical Association , 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
Abstract - Cited by 464 (7 self) - Add to MetaCart
matrix obtained by regressing the series onto its first lag. Critical values for the tests are tabulated, and their power is examined in a Monte Carlo study. Economic time series are often modeled as having a unit root in their autoregressive representation, or (equivalently) as containing a stochastic

Similarity estimation techniques from rounding algorithms

by Moses S. Charikar - In Proc. of 34th STOC , 2002
"... A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, Prh∈F[h(x) = h(y)] = sim(x,y), where sim(x,y) ∈ [0, 1] is some similarity function defined on the collection of objects. Such a scheme leads ..."
Abstract - Cited by 449 (6 self) - Add to MetaCart
to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality

Policy gradient methods for reinforcement learning with function approximation.

by Richard S Sutton , David Mcallester , Satinder Singh , Yishay Mansour - In NIPS, , 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract - Cited by 439 (20 self) - Add to MetaCart
Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly

Locality Preserving Projection,"

by Xiaofei He , Partha Niyogi - Neural Information Processing System, , 2004
"... Abstract Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the ..."
Abstract - Cited by 414 (16 self) - Add to MetaCart
of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) -a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving

Discriminative probabilistic models for relational data

by Ben Taskar , 2002
"... In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels of linked pages are highly correlated. A standard approach is to classify each entity independently, igno ..."
Abstract - Cited by 415 (12 self) - Add to MetaCart
, ignoring the correlations between them. Recently, Probabilistic Relational Models, a relational version of Bayesian networks, were used to define a joint probabilistic model for a collection of related entities. In this paper, we present an alternative framework that builds on (conditional) Markov networks

Front End Factor Analysis for Speaker Verification

by Najim Dehak, Patrick J. Kenny, Réda Dehak, Pierre Dumouchel, Pierre Ouellet - IEEE Transactions on Audio, Speech and Language Processing , 2010
"... Abstract—This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space ..."
Abstract - Cited by 315 (22 self) - Add to MetaCart
Abstract—This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability

Robust object recognition with cortex-like mechanisms

by Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, Tomaso Poggio - IEEE Trans. Pattern Analysis and Machine Intelligence , 2007
"... Abstract—We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating b ..."
Abstract - Cited by 389 (47 self) - Add to MetaCart
Abstract—We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating

External Cognition: How do Graphical Representations Work?

by Mike Scaife, Yvonne Rogers - INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES , 1996
"... Advances in graphical technology have now made it possible for us to interact with information in innovative ways, most notably by exploring multimedia environments and by manipulating three-dimensional virtual worlds. Many benefits have been claimed for this new kind of interactivity, a general ass ..."
Abstract - Cited by 341 (27 self) - Add to MetaCart
critique the disparate literature on graphical representations, focusing on four representative studies. Our analysis reveals a fragmented and poorly understood account of how graphical representations work, exposing a number of assumptions and fallacies. As an alternative we propose a new agenda

Reachability Analysis of Pushdown Automata: Application to Model-Checking

by Ahmed Bouajjani, Javier Esparza, Oded Maler , 1997
"... We apply the symbolic analysis principle to pushdown systems. We represent (possibly infinite) sets of configurations of such systems by means of finite-state automata. In order to reason in a uniform way about analysis problems involving both existential and universal path quantification (like mode ..."
Abstract - Cited by 376 (38 self) - Add to MetaCart
model-checking for branching-time logics), we consider the more general class of alternating pushdown systems and use alternating finite-state automata as a representation structure for their sets of configurations. We give a simple and natural procedure to compute sets of predecessors
Results 11 - 20 of 10,099