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Loss Functions
"... Abstract Vapnik described the “three main learning problems ” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functions used to evaluate performance (01 loss, squared loss and log loss respectively). But there are many other loss function ..."
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Abstract Vapnik described the “three main learning problems ” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functions used to evaluate performance (01 loss, squared loss and log loss respectively). But there are many other loss
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
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Cited by 997 (12 self)
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for additive expansions based on any tting criterion. Specic algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classication. Special enhancements are derived for the particular case where the individual
Comparing Predictive Accuracy
 JOURNAL OF BUSINESS AND ECONOMIC STATISTICS, 13, 253265
, 1995
"... We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic, and need not even be symmetri ..."
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Cited by 1339 (23 self)
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We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic, and need not even
Bias plus variance decomposition for zeroone loss functions
 In Machine Learning: Proceedings of the Thirteenth International Conference
, 1996
"... We present a biasvariance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The biasvariance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no ..."
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Cited by 212 (5 self)
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We present a biasvariance decomposition of expected misclassi cation rate, the most commonly used loss function in supervised classi cation learning. The biasvariance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet
Advances in Prospect Theory: Cumulative Representation of Uncertainty
 JOURNAL OF RISK AND UNCERTAINTY, 5:297323 (1992)
, 1992
"... We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows differ ..."
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Cited by 1710 (17 self)
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different weighting functions for gains and for losses. Two principles, diminishing sensitivity and loss aversion, are invoked to explain the characteristic curvature of the value function and the weighting functions. A review of the experimental evidence and the results of a new experiment confirm a
The Inflation Forecast and the Loss Function
 Central Banking, Monetary Theory and Practice: Essays in Honour of Charles
, 2003
"... This paper argues that inflationtargeting central banks should announce explicit loss function with numerical relative weights on outputgap stabilization and use and announce optimal timevarying instrumentrate paths and corresponding inflation and outputgap forecasts. Simple voting procedures f ..."
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Cited by 26 (6 self)
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This paper argues that inflationtargeting central banks should announce explicit loss function with numerical relative weights on outputgap stabilization and use and announce optimal timevarying instrumentrate paths and corresponding inflation and outputgap forecasts. Simple voting procedures
Loss FunctionBased Evaluation of DSGE Models
 Journal of Applied Econometrics
, 2000
"... In this paper we propose a Bayesian econometric procedure for the evaluation and comparison of DSGE models. Unlike in many previous econometric approaches we explicitly take into account the possibility that the DSGE models are misspecified and introduce a reference model to complete the model space ..."
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Cited by 177 (28 self)
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space. Three loss functions are proposed to assess the discrepancy between DSGE model predictions and an overall posterior distribution of population characteristics that the researcher is trying to match. The evaluation procedure is applied to the comparison of a standard cashinadvance (CIA) and a
Modeling TCP Throughput: A Simple Model and its Empirical Validation
, 1998
"... In this paper we develop a simple analytic characterization of the steady state throughput, as a function of loss rate and round trip time for a bulk transfer TCP flow, i.e., a flow with an unlimited amount of data to send. Unlike the models in [6, 7, 10], our model captures not only the behavior of ..."
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Cited by 1339 (36 self)
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In this paper we develop a simple analytic characterization of the steady state throughput, as a function of loss rate and round trip time for a bulk transfer TCP flow, i.e., a flow with an unlimited amount of data to send. Unlike the models in [6, 7, 10], our model captures not only the behavior
Equationbased congestion control for unicast applications
 SIGCOMM '00
, 2000
"... This paper proposes a mechanism for equationbased congestion control for unicast traffic. Most besteffort traffic in the current Internet is wellserved by the dominant transport protocol, TCP. However, traffic such as besteffort unicast streaming multimedia could find use for a TCPfriendly cong ..."
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Cited by 832 (29 self)
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friendly congestion control mechanism that refrains from reducing the sending rate in half in response to a single packet drop. With our mechanism, the sender explicitly adjusts its sending rate as a function of the measured rate of loss events, where a loss event consists of one or more packets dropped within a
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