نتایج جستجو برای: overfitting

تعداد نتایج: 4333  

1994
Daniel Osman Jacek Mańdziuk

In this paper we compare the results of applying TD(λ) and TDLeaf(λ) algorithms to the game of give-away checkers. Experiments show comparable performance of both algorithms in general, although TDLeaf(λ) seems to be less vulnerable to weight over-fitting. Additional experiments were also performed in order to test three learning strategies used in self-play. The best performance was achieved w...

2012
Jingchen Liu Scott McCloskey Yanxi Liu

We address the problem of complicated event categorization from a large dataset of videos “in the wild”, where multiple classifiers are applied independently to evaluate each video with a ‘likelihood’ score. The core contribution of this paper is a local expert forest model for meta-level score fusion for event detection under heavily imbalanced class distributions. Our motivation is to adapt t...

2008
Johannes Partzsch Christian Mayr René Schüffny

The Bienenstock-Cooper-Munroe (BCM) rule is one of the best-established learning formalisms for neural tissue. However, as it is based on pulse rates, it can not account for recent spike-based experimental protocols that have led to spike timing dependent plasticity (STDP) rules. At the same time, STDP is being challenged by experiments exhibiting more complex timing rules (e.g. triplets) as we...

Journal: :Fuzzy Sets and Systems 2010
Vali Derhami Vahid Johari Majd Majid Nili Ahmadabadi

This paper offers a fuzzy balance management scheme between exploration and exploitation, which can be implemented in any critic-only fuzzy reinforcement learning method. The paper, however, focuses on a newly developed continuous reinforcement learning method, called fuzzy Sarsa learning (FSL) due to its advantages. Establishing balance greatly depends on the accuracy of action value function ...

Journal: :CoRR 2013
Victor Bellon Jesús Cerquides Ivo Grosse

Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal predictions and is known to be robust to overfitting. In this work we introd...

Journal: :Int. J. Fuzzy Logic and Intelligent Systems 2010
Lae-Jeong Park

This paper proposes a new spatial regularization of Fisher linear discriminant analysis (LDA) to reduce the overfitting due to small size sample (SSS) problem in face recognition. Many regularized LDAs have been proposed to alleviate the overfitting by regularizing an estimate of the within-class scatter matrix. Spatial regularization methods have been suggested that make the discriminant vecto...

Journal: :Journal of Machine Learning Research 2014
Nitish Srivastava Geoffrey E. Hinton Alex Krizhevsky Ilya Sutskever Ruslan Salakhutdinov

Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly d...

Journal: :IEEE transactions on neural networks 2002
Chen-Chia Chuang Shun-Feng Su Jin-Tsong Jeng Chih-Ching Hsiao

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outli...

2008
W.M.P. van der Aalst

Process mining techniques attempt to extract non-trivial and useful information from event logs. One aspect of process mining is control-flow discovery, i.e., automatically constructing a process model (e.g., a Petri net) describing the causal dependencies between activities. One of the essential problems in process mining is that one cannot assume to have seen all possible behavior. At best, o...

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