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Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LSSVM classifier, the sparseness property of SVMs is lost due to the choice of the 2norm. Sparseness can be imposed in a second
A binary feedback scheme for congestion avoidance in computer networks
 ACM TRANSACTIONS ON COMPUTER SYSTEMS
, 1990
"... We propose a scheme for congestion avoidance in networks using a connectionless protocol at the network layer. The scheme uses feedback from the network to the users of the network. The interesting challenge for the scheme is to use a minimal amount of feedback (one bit in each packet) from the netw ..."
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Cited by 352 (23 self)
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the network. The scheme attempts to maintain fairness in service provided to multiple sources. This paper presents the scheme and the analysis that went into the choice of the various decision mechanisms. We also address the performance of the scheme under transient changes in the network
Nonlinear Neural Networks: Principles, Mechanisms, and Architectures
, 1988
"... An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentiethcentury scientific movements. The nonlinear, nonstatio ..."
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Cited by 262 (21 self)
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, nonstationary, and nonlocal nature of behavioral and brain data are emphasized. Three sources of contemporary neural network researchthe binary, linear, and continuousnonlinear modelsare noted. The remainder of the article describes results about continuousnonlinear models: Many models of content
2004): “Endogeneity in Semiparametric Binary Response Models,”Review of Economic Studies
"... This paper develops and implements semiparametric methods for estimating binary response (binary choice) models with continuous endogenous regressors. It extends existing results on semiparametric estimation in single index binary response models to the case of endogenous regressors. It develops a c ..."
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Cited by 157 (8 self)
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This paper develops and implements semiparametric methods for estimating binary response (binary choice) models with continuous endogenous regressors. It extends existing results on semiparametric estimation in single index binary response models to the case of endogenous regressors. It develops a
OneClass SVMs for Document Classification
 Journal of Machine Learning Research
, 2001
"... We implemented versions of the SVM appropriate for oneclass classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of oneclass SV ..."
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Cited by 185 (3 self)
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class SVM based on identifying “outlier ” data as representative of the secondclass. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tfidf representation and a modification called “Hadamard
Behavioral theories and the neurophysiology of reward,
 Annu. Rev. Psychol.
, 2006
"... ■ Abstract The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can ..."
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Cited by 187 (0 self)
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for the whole journey. The common denominator in these tasks appears to relate to the anticipation of outcomes of behavior in situations with varying degrees of uncertainty: the merely automatic salivation of a dog without much alternative, the choice of sophisticated but partly unknown liquids, or the well
Nonstationary binary choice
 Econometrica
, 2000
"... This paper develops an asymptotic theory for time series binary choice models with nonstationary explanatory variables generated as integrated processes. Both logit and probit models are covered. The maximum likelihood Ž ML. estimator is consistent but a new phenomenon arises in its limit distributi ..."
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Cited by 17 (8 self)
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This paper develops an asymptotic theory for time series binary choice models with nonstationary explanatory variables generated as integrated processes. Both logit and probit models are covered. The maximum likelihood Ž ML. estimator is consistent but a new phenomenon arises in its limit
Reward Contexts Extend Dop
"... vational value to contextual background via Pavlovian conditioning, and the motivational value then ‘‘spills over’ ’ to the Because pictures may constitute genuine reward for monkeys [11, 12], we assessed their potential reward value withpictures alone, we added identical juice to both optionsand ..."
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reproduction in any medium, provided the original author and source (preventing reward conditioning by testing after all neuronalare credited.2Present address: Fukushima Medical University, 1 Hikarigaoka,binary ocular choices between their respective CSs (Figure 1G). The animals preferred juice over any
BINARY CHOICE GAMES.
"... Abstract The aim of the paper is the construction of a distributional model which enables the study of the evolutionary dynamics that arise for symmetric games, and the equilibrium selection mechanisms that originate from such processes. The evolution of probability distributions over the state vari ..."
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variables is studied using the FokkerPlanck diffusion equation. Equilibrium selection using the ’’basin of attraction’ ’ approach, and a selection process suggested by Pontryagin are contrasted. Examples are provided for all generic 2person symmetric binary choice games. JEL Classification: C78
Binary Choice Models By
, 2009
"... Abstract: We propose a nonparametric approach for estimating singleindex, binarychoice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without specifying a ..."
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a parametric probability function a priori; second, we estimate the unknown probability function using kernel regression of the binary choice variable on the single index estimated in the first step. The estimated probability functions for different demographic groups indicate that the conventional
Results 1  10
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