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[PDF] Top 20 A Generalization Error for Q-Learning

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A Generalization Error for Q-Learning

A Generalization Error for Q-Learning

... of Q-learning with dynamic programming and function approxima- tion for this planning ...between Q-learning and the goal of learning a policy that maximizes the value function has ... See full document

25

Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds

Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds

... the learning algorithm should return identical maps for two training sets with samples differing from each other only by a permuta- ...empirical error of a mapping f S : X → Y learned from the ... See full document

8

Generalization Error Bounds for Threshold Decision Lists

Generalization Error Bounds for Threshold Decision Lists

... The representational properties of threshold decision lists and multilevel threshold functions have been studied by a number of researchers, particularly in the context of Boolean functions. We mentioned above the paper ... See full document

29

Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption

Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption

... poorer learning performance and even though we do not discuss any implementation based on direct methods, it may well be the case that the latter perform better in ... See full document

24

Generalization Error Bounds for Bayesian Mixture Algorithms

Generalization Error Bounds for Bayesian Mixture Algorithms

... Several open issues remain for future research. First, it would be be interesting to combine the current approach with recent methods based on local Rademacher complexities (e.g., Bartlett et al., 2002a), which are ... See full document

22

Analysis of Variance of Cross-Validation Estimators of the Generalization Error

Analysis of Variance of Cross-Validation Estimators of the Generalization Error

... We compared the moment approximation estimators with an estimator proposed by Nadeau and Bengio (2003). The results indicate that the moment approximation estimators perform better in terms of both, variance and bias, ... See full document

42

On modified Dunkl generalization of Szász operators via q calculus

On modified Dunkl generalization of Szász operators via q calculus

... The purpose of this paper is to introduce a modification of q-Dunkl generalization of exponential functions. These types of operators enable better error estimation on the interval [ 1 2 ,∞) than the ... See full document

12

A Novel Routing Algorithm for Mobile ad-hoc Networks Based on Q-learning and its Generalization to FSR Routing Protocol

A Novel Routing Algorithm for Mobile ad-hoc Networks Based on Q-learning and its Generalization to FSR Routing Protocol

... using Q-learning. Q-learning is a machine learning (artificial intelligence) algorithm using the reinforcement learning policy which can be used to solve problems for which there ... See full document

6

Computable Shell Decomposition Bounds

Computable Shell Decomposition Bounds

... in learning curves such as phase ...the generalization error of a hypothesis computable from its training error and the histogram of training errors for the hypotheses in the ...training ... See full document

19

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error

Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error

... test error of (ALICE), (W), (OW), and (O) normalized by the test error of (Passive): For each run, the test errors of (ALICE), (W), (OW), and (O) are divided by the test error of (Passive), and then ... See full document

26

Internally Driven Q-learning - Convergence and Generalization Results

Internally Driven Q-learning - Convergence and Generalization Results

... reinforcement learning problem in which agents are provided with internal drives against which they evaluate the value of the states according to a similarity ...extend Q-learning by substituting ... See full document

5

Certifying the True Error: Machine Learning in Coq with Verified Generalization Guarantees

Certifying the True Error: Machine Learning in Coq with Verified Generalization Guarantees

... Machine learning in interactive theorem ...and learning algorithms, and in- tegration of external tools such as ...that learning procedures generalize, not that each iteration of a learning ... See full document

8

Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation

Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation

... A learning system consists of data, a learning model and a learning ...factors. Learning models for analyzing such data should likewise have complicated ...Hierarchical learning models ... See full document

30

On the (p,q)−Stancu Generalization of a Genuine Baskakov-Durrmeyer Type Operators

On the (p,q)−Stancu Generalization of a Genuine Baskakov-Durrmeyer Type Operators

... The generalization of (p, q)− calculus was introduced by Sahai and Yadav in ...(p, q)− generalizations a sequence of linear positive operators have been published in ...type generalization, ... See full document

8

Using simulation to explore the impact of device design on the learning and performance of peripheral intravenous cannulation

Using simulation to explore the impact of device design on the learning and performance of peripheral intravenous cannulation

... There are a number of limitations to this study that should be acknowledged. First, the lack of formal consid- eration of sample size in advance of the conduct of the study and reliance on non-random, convenience sam- ... See full document

9

Finite sample and asymptotic analysis of generalization ability with an application to penalized regression

Finite sample and asymptotic analysis of generalization ability with an application to penalized regression

... 2. (Tuning the cross-validation hyperparameter K) Eq. (4) characterizes how the hyperparameter K affects the averaged eGE from cross-validation (also called the cross-validation error in the literature). As ... See full document

48

A short note on approximation properties of Stancu generalization of q-Durrmeyer operators

A short note on approximation properties of Stancu generalization of q-Durrmeyer operators

... 13. Phillips, GM: Interpolation and Approximation by Polynomials. CMS Books in Math. Springer, New York (2003) 14. Il’inskii, A, Ostrovska, S: Convergence of generalized Bernstein polynomials. J. Approx. Theory 116(1), ... See full document

5

Approximation results on Dunkl generalization of Phillips operators via q calculus

Approximation results on Dunkl generalization of Phillips operators via q calculus

... Dunkl generalization with an exponential function (see ...studied q-Hermite type polynomials and gave definitions of q-Dunkl analogues of exponential functions and recursion formula as ... See full document

14

On the convergence of a kind of q gamma operators

On the convergence of a kind of q gamma operators

... of q-calculus in the approximation theory is one of the main areas of ...After q-Bernstein polynomials were introduced by Phillips [] in , many researchers have performed studies in this field; we ... See full document

9

A Framework for Mobile Learning for the enhancement of Learning in Higher Education

A Framework for Mobile Learning for the enhancement of Learning in Higher Education

... mobile learning becomes increasingly pervasive, many higher education institutions have initiated a number of mobile learning initiatives to support their traditional learning ...mobile ... See full document

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