Results 1  10
of
10,409
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 ..."
Abstract

Cited by 1000 (13 self)
 Add to MetaCart
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
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
Abstract

Cited by 561 (20 self)
 Add to MetaCart
given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including supportvector machines, AdaBoost, regression, logistic regression and decisiontree algorithms. We also give a multiclass
Random forests
 Machine Learning
, 2001
"... Abstract. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the fo ..."
Abstract

Cited by 3613 (2 self)
 Add to MetaCart
in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
Abstract

Cited by 727 (18 self)
 Add to MetaCart
search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborativefiltering task for making movie recommendations. Here, we present results comparing Rank
Motivational and selfregulated learning components of classroom academic performance
 Journal of Educational Psychology
, 1990
"... A correlational study examined relationships between motivational orientation, selfregulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A selfreport measure of student selfefficacy, intrinsic value, test anxiety, selfregulat ..."
Abstract

Cited by 679 (6 self)
 Add to MetaCart
regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Selfefficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self
No Longer Confidential: Estimating the Confidence of Individual Regression Predictions
"... Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regres ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how
Towards Reliable Reliability Estimates for Individual Regression Predictions
"... In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (such as relative mean squared error). Individual reliability estimates may represent a decisive information in risksens ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
by the ideas from the related work. The results, obtained using 8 regression models and 28 domains indicate the potential for the usage of a sensitivitybased estimate, as well as the approach to the local modeling of prediction error, with the regression trees. By combining pairs of individual estimates, we
Testing for Common Trends
 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
Determinants of longterm growth: a Bayesian Averaging of Classical Estimates (BACE) approach
, 2003
"... This paper examines the robustness and joint interaction of explanatory variables in crosscountry economic growth regressions. It employs a novel approach, Bayesian Averaging of Classical Estimates (BACE), which constructs estimates as a weighted average of OLS estimates for every possible combina ..."
Abstract

Cited by 374 (3 self)
 Add to MetaCart
combination of included variables. The weights applied to individual regressions are justified on Bayesian grounds in a way similar to the wellknown Schwarz model selection criterion. Of 67 explanatory variables we find 18 to be robustly partially correlated with longterm growth and another three variables
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
Abstract

Cited by 422 (2 self)
 Add to MetaCart
ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression
Results 1  10
of
10,409