Semi-Supervised learning (SSL) method has recently caught much attention in the fields of machine learning and computer vision owing to its superiority in classifying abundant unlabelled samples using a few labeled sa...
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Semi-Supervised learning (SSL) method has recently caught much attention in the fields of machine learning and computer vision owing to its superiority in classifying abundant unlabelled samples using a few labeled samples. The goal of this paper is to provide an experimental efficiency comparison between graph based sa algorithms and traditional supervised learning algorithms (e.g., support vector machines) for multispectral image classification. This research shows that SSL algorithms generally outperform supervised learning algorithms in both classification accuracy and anti-noise ability. In the experiments carried out on two data sets (hyperspectral image and Landsat image), the mean overall accuracies (OAs) of supervised learning algorithms are 15 percent and 86 percent, while the mean OAs of SSL algorithms are 26 percent and 99 percent. To overcome the polynomial complexity of SSL algorithms, we also developed a linear-complexity algorithm by employing multivariate Taylor Series Expansion (TSE) and Woodbury Formula.
We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic alg...
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We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant eta by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant eta by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. (C) 2011 Wiley Periodicals, Inc.
We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well known that the celebrated (s, S) policy is optimal. In this...
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We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well known that the celebrated (s, S) policy is optimal. In this paper, we assume the firm does not know the demand distribution a priori and makes adaptive inventory ordering decisions in each period based only on the past sales (a.k.a. censored demand). Our performance measure is regret, which is the cost difference between a feasible learning algorithm and the clairvoyant (full-information) benchmark. Compared with prior literature, the key difficulty of this problem lies in the loss of joint convexity of the objective function as a result of the presence of fixed cost. Wedevelop the first learning algorithm, termed the (d, S) policy, that combines the power of stochastic gradient descent, bandit controls, and simulation-based methods in a seamless and nontrivial fashion. We prove that the cumulative regret is O(log T root T), which is provably tight up to a logarithmic factor. We also develop several technical results that are of independent interest. We believe that the developed framework could be widely applied to learning other important stochastic systems with partial convexity in the objectives.
learning algorithms have extensively been applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especia...
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learning algorithms have extensively been applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especially how these algorithms could be used to classify different structural conditions. Nevertheless, few works present techniques in which vibration signatures can be directly used to provide insights about possible modification processes. This paper proposes a novel approach in which the concept of Symbolic Data Analysis (SDA) is introduced to manipulate not only vibration data (signals) but also modal properties (natural frequencies and mode shapes). These quantities (transformed into symbolic data) are combined to three well-known classification techniques: Bayesian Decision Trees, Neural Networks and Support Vector Machines. The objective is to explore the efficiency of this combined methodology. For this purpose, several numerical simulations are first performed for evaluating the probabilities of true detection (or true classification) in the presence of different damage conditions. Several noise levels are also applied to the data to attest the sensibility of each technique. Second, a set of experimental tests performed on a railway bridge in France is used to emphasize advantages and drawbacks of the proposed approach. Results show that the analysis combining the cited learning algorithms with the symbolic data concepts is efficient enough to classify and discriminate structural modifications with a high probability of true detection, either considering vibration data or modal parameters. Copyright (c) 2010 John Wiley & Sons, Ltd.
Multispectral LiDAR, characterization of completeness, and consistency of spectrum and spatial geometric data provide a new data source for land cover classification. However, how to choose the optimal features for a ...
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Multispectral LiDAR, characterization of completeness, and consistency of spectrum and spatial geometric data provide a new data source for land cover classification. However, how to choose the optimal features for a given set of land covers is an open problem for effective land cover classification. To address this problem, we propose a comparative scheme, which investigates a popular deep learning (deep Boltzmann machine, DBM) model for high-level feature representation and widely used machine learning methods for low-level feature extraction and selection [principal component analysis (PCA) and random forest (RF)] in land cover classification. The comparative study was conducted on the multispectral LiDAR point clouds, acquired by a Teledyne Optech's Titan airborne system. The deep learning-based highlevel feature representation experimental results showed that, on an ordinary personal computer or workstation, this method required larger training samples andmore computational complexity than the machine learning-based low-level feature extraction and selection methods. However, our comparative experiments demonstrated that the classification accuracies of the DBM-based method were higher than those of the RF-based and PCA-based methods using multispectral LiDAR data.
Leather footwear export plays a crucial role in the Indian economy as India is the second largest footwear producer in the world. As a commodity, it is unavoidable to emphasize its export performance by forecasting. T...
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Leather footwear export plays a crucial role in the Indian economy as India is the second largest footwear producer in the world. As a commodity, it is unavoidable to emphasize its export performance by forecasting. This paper aims to bring out an Artificial Neural Network based model to predict India's leather footwear export. Towards forecasting Leather footwear export, the dataset comprising five commodities covered under leather footwear has been taken from 1996 to 97 to 2021-22. The authors have proposed India's Leather Footwear Export -Artificial Neural Network (ILFE-ANN) model with SGD optimizer and activation functions such as Sigmoid / Logistic and Rectified linear unit (ReLU). The authors have kept null values as it is in the data than replacing them with imputation methods such as mean and median while modelling. Outliers are replaced with the mean value of the remaining data before modelling. Moreover, different learning algorithms such as Adaptive Moment (Adam), RMS Propagation (RMSProp), Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) have been compared to choose an optimal one before being implemented in the ILFE-ANN. The vali-dation of the ILFE-ANN model has been implemented for the prediction of livestock population and compared with the Regression model. The variation percentage confirms that the proposed ILFE-ANN model performs significantly with 0.51% RMSProp, 1.68 % SGDM and 2.54 % SGD. Further, the minimum value of performance metrics MAE, MAPE and RMSE obtained are 0.4, 0.5 and 0.5 respectively for the prediction of sheep population for the year 2017. It shows that SGD performs better with the least error rate of 8% MAPE for the export of leather Commodity 64035113. Hence, the study confirms that the ANN model with SGD optimizer performs better for the prediction of India's leather footwear trade data.
The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian netwo...
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The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. The main goal of this paper is to study whether the algorithms for automatically learning the structure of a Bayesian network from data can obtain better results by using this prior knowledge. Three types of restrictions are formally defined: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions. We analyze the possible interactions between these types of restrictions and also how the restrictions can be managed within Bayesian network learning algorithms based on both the score + search and conditional independence paradigms. Then we particularize our study to two classical learning algorithms: a local search algorithm guided by a scoring function, with the operators of arc addition, arc removal and arc reversal, and the PC algorithm. We also carry out experiments using these two algorithms on several data sets. (C) 2006 Elsevier Inc. All rights reserved.
We develop the first nonparametric learning algorithm for periodic-review perishable inventory systems. In contrast to the classical perishable inventory literature, we assume that the firm does not know the demand di...
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We develop the first nonparametric learning algorithm for periodic-review perishable inventory systems. In contrast to the classical perishable inventory literature, we assume that the firm does not know the demand distribution a priori and makes replenishment decisions in each period based only on the past sales (censored demand) data. It is well known that even with complete information about the demand distribution a priori, the optimal policy for this problem does not possess a simple structure. Motivated by the studies in the literature showing that base-stock policies perform near optimal in these systems, we focus on finding the best base-stock policy. We first establish a convexity result, showing that the total holding, lost sales and outdating cost is convex in the base-stock level. Then, we develop a nonparametric learning algorithm that generates a sequence of order-up-to levels whose running average cost converges to the cost of the optimal base-stock policy. We establish a square-root convergence rate of the proposed algorithm, which is the best possible. Our algorithm and analyses require a novel method for computing a valid cycle subgradient and the construction of a bridging problem, which significantly departs from previous studies.
Using GPS technology in the collection of household travel data has been gaining importance as the technology matures. This paper documents recent developments in the field of GPS travel surveying and ways in which GP...
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Using GPS technology in the collection of household travel data has been gaining importance as the technology matures. This paper documents recent developments in the field of GPS travel surveying and ways in which GPS has been incorporated into or even replaced traditional household travel survey methods. A new household activity survey is presented which uses automated data reduction methods to determine activity and travel locations based on a series of heuristics developed from land-use data and travel characteristics. The algorithms are used in an internet-based prompted recall survey which utilizes advanced learning algorithms to reduce the burden placed on survey respondents. Initial results of a small pilot study are discussed and potential areas of future work are presented.
A constructive learning algorithm is used to generate networks that learn to approximate the functional of the magnetotelluric inverse problem, Based on synthetic data, several experiments are performed in order to ge...
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A constructive learning algorithm is used to generate networks that learn to approximate the functional of the magnetotelluric inverse problem, Based on synthetic data, several experiments are performed in order to generate and test the neural networks, Rather than producing, at the present time, a practical algorithm using this approach, the object of the paper is to explore the possibilities offered by the new tools, The generated networks can be used as an internal module in a more general inversion program, or their predicted models can be used by themselves or simply as inputs to an optimization program.
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