It is wellknown that gradient search fails in adaptive IIR filters, since their mean-square error surfaces may be multi-modal. In the letter a new approach based on learning algorithms is shown to be capable of perfor...
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It is wellknown that gradient search fails in adaptive IIR filters, since their mean-square error surfaces may be multi-modal. In the letter a new approach based on learning algorithms is shown to be capable of performing global optimisation. The new algorithms are suitable for both adaptive FIR and IIR filters.
Principal component analysis (PCA) and minor component analysis (MCA) are a powerful methodology for a wide variety of applications such as pattern recognition and signal processing. In this paper, we first propose a ...
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Principal component analysis (PCA) and minor component analysis (MCA) are a powerful methodology for a wide variety of applications such as pattern recognition and signal processing. In this paper, we first propose a differential equation for the generalized eigenvalue problem. We prove that the stable points of this differential equation are the eigenvectors corresponding to the largest eigenvalue, Eased on this generalized differential equation, a class of PCA and MCA learning algorithms can be obtained. We demonstrate that many existing PCA and MCA learning algorithms are special cases of this class, and this class includes some new and simpler MCA learning algorithms. Our results show that all the learning algorithms of this class have the same order of convergence speed, and they are robust to implementation error.
The online networking world is developing step by step; individuals are utilizing web based platforms to communicate their feelings. The tremendous amount of information delivered by such platforms can be analyzed to ...
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Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) t...
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Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation. Areas covered: Different feature selection methods and various linear and nonlinear learning algorithms are employed to address the complexity of data sets for selection of appropriate features important for the responses being modeled, to reduce overfitting of the models, and to derive interpretable models. This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers. Expert opinion: Novel sets of descriptors are being continuously introduced to this field;therefore, to handle this issue, there is a need to improve new tools for feature selection, which can lead to development of statistically meaningful models, usable by nonexperts in the fields. While handling data sets of limited size, special techniques like double cross-validation and consensus modeling might be more meaningful in order to remove the possibility of bias in descriptor selection.
Forests provide crucial ecosystem services and are increasingly threatened by invasive plant species. The spread of these invasive species has affected biodiversity and has become a trending topic due to its impact on...
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Forests provide crucial ecosystem services and are increasingly threatened by invasive plant species. The spread of these invasive species has affected biodiversity and has become a trending topic due to its impact on both endemic species and biodiversity. Therefore, it is imperative to implement conservation measures to protect native species such as mapping and monitoring invasive plant species in the forest realm. Mapping understory herb invasive plant species within forest categories is challenging, for example species such as Ageratum conyzoides and Cassia tora do not occur in distinct clusters, making them difficult to distinguish from the surrounding forest. In this paper, phenology plays a vital role for analysing the separability of both inter and intra-species discrimination to examine temporal curves for different vegetation indices that affect plant growth during the green and senescence periods. Machine learning algorithms, including regression tree-based algorithms, decision tree-based algorithms, and probabilistic algorithms, were used to determine the most effective algorithm for pixel-based classification. Support Vector Machine (SVM) classifier was the most effective method, with an overall accuracy of this classifier was calculated as 90.28% and a kappa of 0.88. The findings indicate that machine learning algorithms remain effective for pixel-based classification of understory invasive plant species from forest class. Thus, this study shows a technical method to distinguish invasive plant species from forest class which can help forest managers to locate invasion sites to eradicate them and conserve native biodiversity.
The purpose of this work is the comparison of learning algorithms in continuous time used in optimization and game theory. The first three are issued from no-regret dynamics and cover in particular "Replicator dy...
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The purpose of this work is the comparison of learning algorithms in continuous time used in optimization and game theory. The first three are issued from no-regret dynamics and cover in particular "Replicator dynamics" and "Local projection dynamics". Then we study "Conditional gradient" versus "Global projection" dynamics and finally "Frank-Wolfe" versus "Best reply" dynamics. Important similarities occur when considering potential or dissipative games.
Medical datasets often have a skewed class distribution and a lack of high-quality annotated images. However, deep learning methods require a large amount of labeled data for classification. In this study, we present ...
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algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concav...
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algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial "25 words or less"), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries;that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).
A neural network processing scheme is proposed which utilizes a self-organizing Kohonen feature map as the front end to a feedforward classifer network. The results of a series of benchmarking studies based upon artif...
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A neural network processing scheme is proposed which utilizes a self-organizing Kohonen feature map as the front end to a feedforward classifer network. The results of a series of benchmarking studies based upon artificial statistical pattern recognition tasks indicate that the proposed architecture performs significantly better than conventional feedforward classifer networks when the decision regions are disjoint. This is attributed to the fact that the self-organization process allows internal units in the succeeding classifer network to be sensitive to a specific set of features in the input space at the outset of training.
Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problem...
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Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinfarcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.
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