kernlab - An S4 Package for Kernel Methods in R

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.

Support Vector Machines in R

Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.

A convergent decomposition method for box-constrained optimization problems.

In this work we consider the problem of minimizing a continuously differentiable function over a feasible set defined by box constraints. We present a decomposition method based on the solution of a sequence of subproblems. In particular, we state conditions on the rule for selecting the subproblem variables sufficient to ensure the global convergence of the generated sequence without convexity assumptions. The conditions require to select suitable variables (related to the violation of the optimality conditions) to guarantee theoretical convergence properties, and leave the degree of freedom of selecting any other group of variables to accelerate the convergence. © 2009 Springer-Verlag

Planning the Industrial Estate Area for Comparison Two Methods

Analysis cluster method is a popular science in the knowledge of partitioning data in the areas of engineering, medicine, economics and statistics are used to perform machine learning, data mining and other data grouping. This method is often used because it is very easy and able to partition data quickly on large data sets. Cluster analysis plays an important role in classifying the object, depending on the application; object can be in the form of signals, customers, patients, news, and other plants. This technique is a nonparametric technique which is very much applicable in real cases. Clustering techniques can be grouped into two major classes: partitioning the cluster (K-means) and hierarchical cluster. There are two kinds of clustering techniques are often used. The first is the K-means and the second is a hierarchical clustering. In this research uses of investment data as the primary data source will be analyzed by comparing the two algorithm methods, both of the above algorithm to find the final solution using high-level programming language. In this research result that quite the same in application data to planning the industrial estate area.

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure

Parameter selection of Gaussian kernel SVM based on local density of training set

Support vector machine (SVM) is regarded as one of the most effective techniques for supervised learning, while the Gaussian kernel SVM is widely utilized due to its excellent performance capabilities. To ensure high performance of models, hyperparameters, i.e. kernel width and penalty factor must be determined appropriately. This paper studies the influence of hyperparameters on the Gaussian kernel SVM when such hyperparameters attain an extreme value (0 or ∞). In order to improve computing efficiency, a parameter optimization method based on the local density and accuracy of Leave-One-Out (LOO) method are proposed. Kernel width of each sample is determined based on the local density needed to ensure a higher separability in feature space while the penalty parameter is determined by an improved grid search using the LOO method. A comparison with grid method is conducted to verify validity of the proposed method. The classification accuracy of five real-life datasets from UCI database are 0.9733, 0.9933, 0.7270, 0.6101 and 0.8867, which is slightly superior to the grid method. The results also demonstrate that this proposed method is computationally cheaper by 1 order of magnitude when compared to the grid method

DEVELOPMENT OF WIRELESS PEBBLE FOR PACKED BED HEAT TRANSFER MEASUREMENTS AND MACHINE LEARNING-AIDED ACCIDENT DIAGNOSIS FOR LOSS OF FLOW ACCIDENT (LOFA)

In the first study, a novel wireless pebble for scale experiments is developed, and a simple heat transfer experiment is conducted to determine the difference in the local heat transfer coefficient. Based on the fact that the use of Dowtherm A between approximately 57–87 °C is an alternative to the normal use of the FliBe temperature range of 600–700°C, a new-concept wireless device in a scaled experiment is introduced. This device consists of a 63.5 mm diameter metal shell and contains a built-in customized circuit board and battery for driving temperature measurements and wireless data transfer. The circuit board used for receiving temperature measurements from several type K thermocouples is based on the ATmega328 microprocessor (MCU). This board collects temperature at multiple points and send data to the receiver wirelessly. Also, the results from the experiment on the wireless communication between multiple devices are presented. The single wireless pebble did not change the value of the averaged heat transfer coefficient, even when the airflow rate changed and the attachment structure of Thermocouple (TC) was changed. Because the averaged heat transfer coefficient did not change significantly in the orientation of the internal heater, it could easily expand into several wireless pebbles. Lastly, the demonstration of three multiple wireless pebbles indicates that our suggested wireless pebbles can greatly help to estimate the local heat transfer coefficient. The results of the validation process will be extended to multiple wireless pebbles in future packed pebble-beds. This research is applicable to a case study for scaled experiments with packed pebble-beds for fluoride salt-cooled high-temperature reactors. We expect that this study will provide an experimental basis for pebbles for those who wish to use a non-invasive wireless device, which offers a powerful approach to investigating heat transfer coefficients in a non-invasive manner and to designing randomly packed configurations for further studies. In the second study, a prediction of accident diagnosis using machine learning was performed. The simple case of an unprotected loss of flow accident (LOFA) was selected for simulation for a fuel pin at the start of different flow rates. The obtained outlet temperatures were used to identify the relationship between peak fuel temperatures and flow rate changes using a support vector machine (SVM). The SVM, trained with the core outlet temperature, provided an accurate prediction (R2 \u3e 0.9) for changes in mass flow rate and peak surface temperature cladding in the early phase of LOFA transience (~0.5 s) unless the amount of training data was limited. This illustrates that the key accident features are reflected well in the reactor core’s early response (i.e., core outlet temperature). This implies two possibilities. The first is to implement a different diagnostic framework from the current behavioral response to a prolonged progression of the accident. The second is to provide effective guidance for accident mitigation strategies in the early stages of accident progression. The high predictability (i.e., R2 \u3e 0.9) presented in the early phase of unprotected LOFA shows that the core outlet temperature is strongly correlated with both the change in flow rate and the peak surface cladding temperature throughout the entire transience. This strong correlation between different physical parameters allows for the possibility of interdependent detector systems by reducing the traditional boundaries of physical location and physical quantities in accident response and progress detection