Radial basis function kernel

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An Approach to Measure Pronunciation Similarity in Second Language Learning Using Radial Basis Function Kernel

An Approach to Measure Pronunciation Similarity in Second Language Learning Using Radial Basis Function Kernel

In this paper, a PED method based on psychoacoustic knowledge from a spectral auditory model (van de Par et al., 2002) is presented that models the native perception to evaluate non-native pronunciations based on acoustic and auditory processing of the speech sounds. The fundamental assumption is based on the ability of the human auditory system to distinguish speech sounds of various type. The method compares the acoustic and auditory-perceptual characteristics of uttered phones on a frame-by-frame basis. In doing so, it utilizes a similarity measure based on radial basis function kernel or RBF kernel, which is compared with a Euclidean distance measure that was used in (Koniaris and Engwall, 2011; Koniaris et al., 2013). The motivation for this arrives from the fact that the data become sparse in a high dimensional space and hence choosing RBF kernel seems a more suitable solution since it is considered more appropriate for such conditions (Braun et al., 2008). Roughly speaking, the method performs a comparison between speech sounds generated by a group of native speakers with the corresponding speech sounds generated by different L2 groups of speakers. This is done separately for each phoneme category and the uttered phones are transformed into their auditory representations for the native speech, and into their spectrum representations for the non-native speech. In each domain, a distortion measure based on the RBF kernel is computed for each speech frame and then the two distortion measures are explored – considering all the frames – to investigate, quantitatively, the similarities between the native and the non-native phones.
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Heart Disease Diagnosis Based on Deep Radial Basis Function Kernel Machine

Heart Disease Diagnosis Based on Deep Radial Basis Function Kernel Machine

Over the years Radial Basis Function (RBF) Kernel Machines have been used in Machine Learning tasks, but there are certain flaws that prevent their usage in some up-to-date applications (e.g., some Kernel Machines suffer from fast growth number of learning parameters whilst predicting data with large number of variations). Besides, Kernel Machines with single hidden layer have no mechanisms for features selection in multidimensional data space, and machine-learning task becomes intractable with enlargement of the data available for analysis. To address these issues, this paper investigates the usage of a framework for “deep learning” architecture composed of multilayered adaptive non-linear components – Multilayer RBF Kernel Machine. To be precise, three different approaches of features selection and dimensionality reduction to train RBF based on Multilayer Kernel Learning are explored, and comparisons between them in terms of accuracy, performance and computational complexity are made. As opposed to the “shallow learning” algorithm with usually single layer architecture, results show that the multilayered system produces better results with large and highly varied data. In particular, features selection and dimensionality reduction, as a class of the multilayer method, shows results that are more accurate. This paper proposes a novel scheme based on deep Multilayer RBF Kernel Machine learning for sleep apnea detection and quantification using statistical features of ECG signals. The results obtained show that the newly proposed approach provides significant accuracy improvements compared to state-of-the-art methods. Because of its noninvasive and low-cost nature, this algorithm has the potential for numerous applications in sleep medicine.
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Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression

Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression

Based on the description above, we need to be able to process currency exchange rate data to make the right policy in the future with artificial intelligence. Artificial Intelligence (AI) makes human problems easier to solve and can predict future problems [5]. Using analysis and the best method in AI will reduce the effects of errors and also increase profitability and Machine Learning is one of Artificial Intelligence that could solve a problem from known based on the learning or training data that has been inputted [6-9]. One of the machine learning methods is the Support Vector Regression method. This method is the development of SVM (Support Vector Machine) for regression cases [10]. The purpose of the SVR is to find a function 𝑓 (𝑥) as a hyperplane (dividing line) in the form of a regression function that corresponds to the input data with an error ε and makes ε as thin as possible. The SVR method is used to solve non-linear problems. SVR is successfully applied in several problems in time series prediction [10]. This is what drives this research to use SVR to predict the rupiah exchange rate.
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Using SVM as Back-End Classifier for Language Identification

Using SVM as Back-End Classifier for Language Identification

a back-end radial basis function (RBF) kernel [13] SVM classifier is used to discriminate target languages based on the probability distribution in the discriminative vector space of language characterization scores. The choice of radial basis function kernel is based on its nonlinear mapping function and requirement of relatively small amount of parameters to tune. Furthermore, the linear kernel is a special case of RBF and the sigmoid kernel behaves like radial basis function for certain parameters [14]. Note that the training data of this back-end SVM classifier comes from development data rather than from the data used for training n-gram language models, and cross-validation is employed to select kernel parameters and prevent over-fitting. For testing, once the discriminative language characterization score vectors of a test utterance are generated, the back-end SVM classifier can estimate the posterior probability of each target language that is used to calibrate final outputs. As mentioned above, pair-wise posterior probability estimation (PPPE) algorithm is used to calibrate the output of each classifier. In fact, the multiclass classification problem refers to assigning each of the observations into one of k classes. As two-class problems are much easier to solve, many authors propose to use two- class classifiers for multiclass classification. PPPE algorithm is a popular multiclass classification method that combines all comparisons for each pair of classes. Furthermore, it focuses on techniques that provide a multiclass probability estimate by combining all pair-wise comparisons [15, 16].
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Comparison Of Various Kernels Of Support Vector Machine

Comparison Of Various Kernels Of Support Vector Machine

Abstract— As we know, classification plays an important role in every field. Support vector machine is the popular algorithm for classification and prediction. For classification and prediction by support vector machine, LIBSVM is being used as a tool. Support vector machine classifies the data points using straight line. Some datasets are impossible to separate by straight line. To cope with this problem kernel function is used. The central idea of kernel function is to project points up in a higher dimensional space hoping that separability of data would improve. There are various kernels in the LIBSVM package. In this paper, Support Vector Machine (SVM) is evaluated as classifier with four different kernels namely linear kernel, polynomial kernel, radial basis function kernel and sigmoid kernel. Several datasets are being experimented to find out the performance of various kernels of support vector machines. Based on the best performance result, linear kernel is capable of classifying datasets accurately with the average accuracy 88.20 % of correct classification and faster with 4.078 sec of prediction time. Radial basis function Kernel is capable of taking less training time compared to other kernels that is 4.92675 sec.
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FACIAL EMOTION RECOGNITION THROUGH GABOR FILTER AND RBF NETWORKS

FACIAL EMOTION RECOGNITION THROUGH GABOR FILTER AND RBF NETWORKS

In present work a better emotion detection method for segmented image using weighted Gabor filter and radial basis function kernel has been discussed. The selected filters are considered on the selected feature space domain to decrease the computational complexity without scarifying concert. First face is detected from entire image and then segments the face region to obtain selected features from face (eyes, eyebrow and mouth). These modules of the face are then introduced to Gabor-based method. Then the output feature vectors are classified via the RBF network.
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													Feature based growth rate analysis and  pattern recognition of mesenchymal stem cells

1. Feature based growth rate analysis and pattern recognition of mesenchymal stem cells

Abstract - Stem cell is a generic cell that can make exact copies of it. Stem cells are master cells and can grow into different cell types in the body during evolution. Different parts of your body are made up of different kinds of cells. Stem cells are not specific to one area in the body and can turn into skin, bone, blood or brain cells. Occasionally stem cells do not build up accurately once injecting to human body, which will lead to complicated. To avoid this problem the growth of the stem cell should be monitored with the help of morphological and textural characteristics. Meanwhile, with the rapid development of feature extraction and pattern recognition techniques, it provides people with new thought on the study on complex image retrieval while it’s very difficult for traditional machine learning method to get ideal retrieval results. Pattern Recognition is one of the very important and actively searched trait or branch of artificial intelligence. It is the science which tries to make machines as intelligent as human to recognize patterns and classify them into desired categories in a simple and reliable way. For this reason, proposed a new approach for image features based on Modified Non Subsampled Contourlet Transform (MNSCT), Radial Basis Function Kernel Principal Component Analysis (RBFKPCA), and Relevance Vector Machine (RVM). The proposed method is used to extract the salient features of stem cell images by using efficiency of Modified Non Subsampled Contourlet Transform (MNSCT) that finds the best feature points. Relevance Vector Machine (RVM) is a probabilistic model whose functional form is equivalent to the SVM. It achieves comparable recognition accuracy to the SVM, yet provides a full predictive distribution, and also requires substantially fewer kernel functions.
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C-Support Vector Classification the Estimation of the MS Subgroups Classification with Selected Kernels and Parameters

C-Support Vector Classification the Estimation of the MS Subgroups Classification with Selected Kernels and Parameters

C-SVC (C-Support Vector Classification) is a multiclass Support Vector Machine algorithm method. Data has been applied onto methods such as Radial Basis Function (RBF) kernel, Poly- nomial kernel, Sigmoid kernel and Linear kernel which are considered as C-SVC algorithms [18]. According to C-SVC kernels experimental results have been given by two aspects that are k-fold cross validation and computation time. In the meantime, performance evaluation has been made through k-fold cross validation (k = 10). The computation time during the classification of the C-SVC kernel (Radial Basis Function kernel, Polynomial kernel, Sigmoid kernel, Linear kernel) methods have been provided in a comparative fashion. Please see Table 2 for a brief review of relevant literature.
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													Hilbert transform and rbf-kernel based support vector machine synergy for automatic classification of eeg signals

1. Hilbert transform and rbf-kernel based support vector machine synergy for automatic classification of eeg signals

Abstract: This paper presents a machine learning approach for the development of a computer aided Radial Basis Function kernel based Support Vector Machine (SVM) used to analyze and classify EEG signals. The algorithm proposed in this work is used to achieve timely and accurate detection of the onset of epileptic seizures. As the brain’s electrical activity is composed of numerous signals with overlapping characteristics, it is important to develop a non-invasive method so that epileptic patients receive proper medical attention hours before the seizures occur. In this paper, a feature extractor is developed which is combined with a RBF Kernel based Support Vector Machine to classify epileptic subjects from healthy subjects. Analysis of real time EEG signals is simplified by using the Hilbert Transform which converts the signals into analytic signals. The proposed algorithm is developed using the MATLAB software and the average accuracy of classification is obtained to be 93.89%.
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Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function

Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function

This work showcased a method which cast-off Multiclass Support vector machine (Multi-SVM) with radial basis function kernel methodology to detect/recognize and classify/group several diseases that exists in plant leaves Diseases/ailments such as Cercospora leaf spot,Bacterial blight, Anthracnose, Alternaria alternata, of plant leaves are deliberated for the experimentation. The disease area are segmented via k-means clustering method and Gray level co-occurrence matrix (GLCM) texture features are cast-off for the classification. On the other hand, the classification performance of the Multiclass SVM with RBF kernel on plant leaf disease delivers 97.617 % accuracy and the anticipated method based tactic gives better results on comparing to some of the prevailing methods.
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Support Vector approach by using Radial Kernel Function for Prediction of Software Maintenance Effort on the basis of Multivariate Approach

Support Vector approach by using Radial Kernel Function for Prediction of Software Maintenance Effort on the basis of Multivariate Approach

The continuous dependent variable in our study is testing effort. The goal of our study is to empirically explore the relationship between OO metrics and testing effort at the class level. We use SVM with Gaussian kernel to predict testing effort per class. Testing effort is defined as lines of code changed or added throughput the life cycle of the defect per class. The independent variables are OO metrics chosen for this study. The metrics selected in this study are summarized in Table 1.There are eight independent variables i.e. rfc, nom, wmc, dac, mpc, lcom, noc, dit and one independent variable i.e change in the W.Li and S.Henry Dataset.
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Medium term load 
		forecasting using evolutionary programming least square support vector 
		machine

Medium term load forecasting using evolutionary programming least square support vector machine

This paper presents new intelligent-based technique namely Evolutionary Programming- Least-Square Support Vector Machine (EP-LSSVM) to forecast a medium term load demand. Medium-term electricity load forecasting is a difficult work since the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. Available historical load data are analyzed and appropriate features are selected for the model. Load demand in the year 2008 until 2010 are used for features in combination with day in months and hour in days. There are 3 inputs vectors for this proposed model consists of day, month and year. As for the output, there are 24 outputs vectors for this model which represents the number of hour in a day. In EP-LSSVM, the Radial Basis Function (RBF) Kernel parameters are optimally selected using Evolutionary Programming (EP) optimization technique for accurate prediction. The performance of EP-LSSVM is compared with those obtained from LS-SVM using cross- validation technique in terms of accuracy. The experimental results show that the proposed approach gives better performance in terms of Mean Absolute Percentage Error (MAPE) and coefficients of determination (R 2 ) for the entire period of prediction.
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Intelligent Robotic Inspection System Using Image Processing Technique

Intelligent Robotic Inspection System Using Image Processing Technique

Abstract- The objective of the present research work is to develop an intelligent robotic system to identify and classify the defective objects in a bottle manufacturing unit. At this point Artificial Intelligence procedure followed by the different classifier are used in order to classify the defective bottle. The response of the proposed model highlighted that the Least Square Support Vector machine (LSSVM), Linear Kernel and radial basis function has maximum overall performance in terms of Classification rate comparison to others. Thus, the proposed model is a best suitable for classification in a manufacturing unit. The addition of robotic unit also enhances the performance rate as well as more productivity. Here four various machine learning classifiers like K TH NN, ANN, SVM and LSSVM for grouping of defective bottle images. Hence it is perceived that the proposed LSSVM with RBF and kernel are confirmed the higher rate of 96.35 % matched to other classifiers used during the analysis.
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INTRUSION DETECTION USING KERNELIZED SUPPORT VECTOR MACHINE WITH LEVENBERG- MARQUARDT LEARNING

INTRUSION DETECTION USING KERNELIZED SUPPORT VECTOR MACHINE WITH LEVENBERG- MARQUARDT LEARNING

The major reasons for using SVM in IDS is because of its speed and its scalability. Moreover the classification complexity of SVM is not based on the dimensionality of the feature space and hence it can potentially learn a huge collection of patterns. Also SVM provides a standard mechanism to fit the surface of the hyper plane to the data by utilizing the kernel function. In this paper, radial basis kernel function of SVM is tuned using Levenberg-Marquardt (LM) learning and tested using KDD Cup 1999 dataset based on the detection rate and very low false alarm rate.
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A NEW SOFT SET BASED PRUNING ALGORITHM FOR ENSEMBLE METHOD

A NEW SOFT SET BASED PRUNING ALGORITHM FOR ENSEMBLE METHOD

Thyroid diseases are widespread worldwide. In India too, there is a significant problems caused due to thyroid diseases. Various research studies estimates that about 42 million people in India suffer from thyroid diseases [4]. There are a number of possible thyroid diseases and disorders, including thyroiditis and thyroid cancer. This paper focuses on the classification of two of the most common thyroid disorders are hyperthyroidism and hypothyroidism among the public. The National Institutes of Health (NIH) states that about 1% of Americans suffer from Hyperthyroidism and about 5% suffer from Hypothyroidism. From the global perspective also the classification of thyroid plays a significant role. The conditions for the diagnosis of the disease are closely linked; they have several important differences that affect diagnosis and treatment. The data for this research work is collected from the UCI repository which undergoes preprocessing. The preprocessed data is multivariate in nature. Curse of Dimensionality is followed so that the available 21 attributes is optimized to 10 attributes using Hybrid Differential Evolution Kernel Based Navie Based algorithm. The subset of data is now supplied to Support Vector Machine (SVM) classifier algorithm where Radial Basis Function Kernal(RBF) is used. In order to stabilize the errors this iterative process takes 25 and the data is classified. The accuracy of classification is observed to be 99.89%. This result is efficient when compared to our previous work that used the Kernel based Naïve bayes classifier. Keywords: Classification, Curse of Dimensionality, Differential Evolutionary algorithm, Multivariate
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Adaptive Radial Basis Function Detector for Beamforming

Adaptive Radial Basis Function Detector for Beamforming

Adaptive beamforming is capable of separating user signals transmitted on the same carrier frequency, and thus provides a practical means of supporting multiusers in a space-division multiple-access scenario [1]–[10]. Classically, this is achieved by a linear beamformer based on the minimum mean square error (L-MMSE) solution [1],[2],[7],[8],[11]. The L-MMSE beamforming requires that the number of users supported is no more than the number of receive antenna elements. If this condition is not met, the system is referred to as overloaded or rank-deficient. The optimal solution for the linear beamforming has been shown to be the minimum bit error rate (L-MBER) design [12],[13] which outperforms the L-MMSE one and is capable of operating in hostile rank- deficient scenarios. However, digital communication signal detection can be viewed as a classification problem [14]- [16], where the receiver detector simply classifies the received multidimensional channel-impaired signal into the most-likely transmitted symbol constellation point or class. Both the radial basis function (RBF) network [17]-[19] and other kernel models [20]-[25] have been applied to solve this nonlinear detection problem. All these nonlinear detectors attempt to approximate the underlying optimal Bayesian solution. The standard RBF or kernel modelling technique constitutes a black-box approach that seeks to extract a model representa- tion from the available training data. Adopting this black-box modelling approach is appropriate, if no a priori information exists regarding the underlying data generating mechanism. If however there exists some a priori information concerning the system to be modelled, this a priori information should be incorporated into the modelling process. Many real-life
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FEATURE SELECTION USING MODIFIED ANT COLONY OPTIMIZATION APPROACH (FS MACO) 
BASED FIVE LAYERED ARTIFICIAL NEURAL NETWORK FOR CROSS DOMAIN OPINION MINING

FEATURE SELECTION USING MODIFIED ANT COLONY OPTIMIZATION APPROACH (FS MACO) BASED FIVE LAYERED ARTIFICIAL NEURAL NETWORK FOR CROSS DOMAIN OPINION MINING

With the growth of the internet, network attacks have increased severely in a substantial number in the last few years. Therefore, Intrusion Detection Systems (IDSs) have become a necessary addition to the information security of most organizations. An IDS monitors a network or a single host looking for suspicious activity and reports them. Many intrusion detection types of research have focused on the feature selection because some characteristics are irrelevant or redundant which result in a lengthy detection process and degrades the performance of IDS. For this purpose, we have used in this work an algorithm based on Information Gain technique. This algorithm selects an optimal number of features from NSL-KDD Dataset. In addition, we have combined the feature selection with a machine learning technique named Support Vector Machine (SVM) using Radial-basis kernel function (RBF) and a Particle Swarm Optimization algorithm to optimize the parameters of SVM for effective classification of the dataset. We have also compared the proposed method and other methods. Tests on the NSL-KDD Dataset have proved that our proposed method can reduce the number of features and obtain good results in terms of accuracy, attack detection rate and false positives rate, even for unknown attacks.
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Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and Kth Nearest Neighbours

Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and Kth Nearest Neighbours

This means that a designed pattern recognition system acts to reduce human intervention as much as possible [1]. Pattern recognition systems are comprised of several elements, including input, sensing, segmentation, feature extraction, classification, post-processing and decision [2]. It may be necessary to make adjustments for missing features and context in the classification and post-processing steps. Furthermore, system designers sometimes penalize the last part of the pipeline when the final decision is made [2]. In a pattern recognition system designed for image processing, the input is an image and the output is often an image too [2]. In image processing, one of the most important steps in the analysis of processed image data is image segmentation. The goal of image segmentation is to divide an image into parts that have a strong correlation with the objects being represented in the image (i.e., Binary Segmented Image). Binary Image Segmentation is the process of dividing an image into two classes (e.g., two colors, or black and white) [3]. As mentioned above, one of the most important steps in training an intelligent network is to select efficient parameters for it. This step, which is time- consuming, consists of two major procedures grid search and cross-validation. Intelligent networks such as Support Vector Machines and Neural Networks have complex structures, and their efficiency depends on the network parameters. Thus, the best values for the network parameters must be extracted by grid search and cross-validation. In this study, binary image segmentation was carried out by means of various classification methods Support Vector Machines, Neural Networks and K th Nearest Neighbours algorithms. This study focused primarily on efficient parameter selection for these classification methods, then compared the results of these methods. For SVM, four kernel functions were selected Radial Basis Function, Quadratic, Polynomial (with order of 3), and Linear functions. Neural Networks and K-NN were also applied to datasets. To train and test the networks, five different datasets were used with 100, 500, 1500 and 2000 datapoints. The pixel coordinates (x,y) were considered as the inputs of networks. The average accuracy obtained for SVM, ANN and K-NN were approximately 95%, 88% and 97%, respectively. The SVM-RBF showed a high level of accuracy and consistency for this classification problem. The pixel coordinates that were used as the features in this image classification were recognized as highly discriminative and separable features. Finally, grid search and cross-validation highly increased the level of accuracy and confidence in the results.
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AN APPROACH FOR FEATURES MATCHING BETWEEN BILATERAL IMAGES OF STEREO VISION 
SYSTEM APPLIED FOR AUTOMATED HETEROGENEOUS PLATOON

AN APPROACH FOR FEATURES MATCHING BETWEEN BILATERAL IMAGES OF STEREO VISION SYSTEM APPLIED FOR AUTOMATED HETEROGENEOUS PLATOON

1972 crawling twitter with HyperPipes, Tree J.48, and Support Vector Machine (SVM) method. It was reported that among these methods, J.48 reached accuracy of 86.24%, which was the best among other methods used in this research. For the purpose of the research, Farisia used Margono corpora namely the collection of written and spoken texts. Furthermore, [6]Farisia reported that SVM method did not work well in the research, and suggested that it has to be used by oversampling, oversizing, and using punctuation mark as well as the emoticons on the corpora. [4]Dinakar has done detection of cyber bullying by crawling youTube comments using Naive Bayes, HyperPipes, Tree- J.48, SVM methods as well as the feature space design. [5]Nahar has done detection of cyber bullying by crawling social media using SVM with linear kernel and 10-fold cross validation, as well as feature selection. [6]Desai has done detection of cyber bullying using SVM with Radial Basis Function (RBF) kernel to classify sentences or phrase which contains bullying: #kataksh has accuracy of 78.84%, markers (the positive and negative word, #tag kataksh, and emoticon which includes bullying context) has accuracy of 83.74% and without marker (cue word, odd combination of words, pair of words, and its antonym which includes bullying context) has accuracy of 66%. Kataksh is a Hindi word which means something like insinuation or sarcasm in English word.
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Compactly Supported Radial Basis Function Kernels

Compactly Supported Radial Basis Function Kernels

information loss due to the sparsifying process, and a large sparsity S(C) implies a high degree of sparsity. Theorems 1 and 2 show that both A(C) and S(C) take values in the interval [0, 1]; as C increases, A(C) increases but S(C) decreases. Therefore it is impossible to maximize both scores simultaneously. This similarity-sparsity tradeoff is analogous to the bias-variance tradeoff in statistical modeling to avoid overfitting. We propose three procedures to tune C adaptively, and they are all convenient to implement before any training of the kernel machine takes place.

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