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Telemetry data, containing the data of multiple subsystems such as power system, implies the on-orbit operation status information of the satellite. We can obtain performance characteristics and fault symptom of the satellite subsystems through analyzing these data. Using classification algorithm we can provide normal data for anomaly detection and find the data from various subsystems which have...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
Optimum symbol-decision boundaries created using machine learning approaches such as support-vector machine (SVM) can enhance system performance in the presence of nonlinear signal distortion. However, they fail if the received training signals for boundary creation include cycle slips induced by laser phase noise. In this paper, we propose a novel decision-boundary generation algorithm that is tolerant...
Poses recognition is an important research topic because some situations require silent communication (sign language, surgeon poses to the nurse for assistance etc.). Traditionally, poses recognition requires high quality expensive cameras and complicated computer vision algorithms. This is not the case thanks to the Microsoft Kinect sensor which provides an inexpensive and easy way for real time...
Classification is a challenging phenomenon. Text classification uses terms as features which can be grouped to vote for belongingness of a class. This paper explores the performance of Support Vector Machine (SVM) on variation of text features. Empirical results support the findings. The reported result shows significant degradation in SVM classifier as we reduce features from 100 to 50 and then to...
By maximizing the gap between classes in the reproducing kernel Hilbert space (RKHS), our method optimizes for the sigma values of radial basis function (RBF) or gaussian kernels. For each sample, we try to ensure the distance gap between intra-class and inter-class in RKHS to be large. Unlike previous methods of multiple kernel learning, our method does not need large amount of computations, which...
The difficulties of data streams, i.e. Infinite length, the occurrence of concept-drift and the possible emergence of novel classes, are topics of high relevance in the field of recognition systems. To overcome all of these problems, the system should be updated continuously with new data while the amount of processing time should be kept small. We propose an incremental Parzen window kernel density...
Recent years have witnessed the fast growth of the use of the mobile applications (a.k.a. "apps"). Detecting similar apps is a basic problem in the app ecosystem. It is not only beneficial to app search and recommender systems, but also helpful for people to discover new apps. State-of-the-art studies defined several app similarity functions by the metainformation of apps, such as descriptions...
Support Vector Machines (SVM's) are supervised learning algorithms which can be used for analyzing patterns and classifying data. This supervised algorithm is applicable for binary class as well as multiclass classification. The core idea is to build a hyperplane which can easily separate the training examples. For binary class, SVM constructs a hyper-plane which can easily separate d-dimensional...
Kernel density model works well for limited training data in acoustic modeling. In this paper, we improve the kernel density-based acoustic model for low resource language speech recognition. In our previous study, we demonstrated the effectiveness of the kernel density-based acoustic model on discriminative features such as cross-lingual bottleneck features. In this paper, we propose to learn a Mahalanobis-based...
Traditional algorithms for training the Support Vector Machines (SVMs) have a worst case time complexity of O(n3) and a space complexity of O(n2). This makes it difficult to scale the training algorithm for large scale datasets. In this paper, three algorithms have been proposed for reducing the training dataset. The algorithms mine the potential support vectors based on closeness to decision boundary...
Support vector machines (SVMs) are benchmark developments in the field of machine learning. Recently, various nonparallel hyperplanes classification algorithms (NHCAs) have been proposed, which are comparable in terms of classification accuracy when compared with SVM but are computationally more efficient. All these NHCAs are originally proposed for binary classification problems. Since, most of the...
It is widely recognized that the kernel-based learning scheme is one of powerful tools in the field of machine learning. Recently, learning with multiple kernels, instead of a single kernel, attracts much attention in this field. Although their efficacy was investigated in terms of practical sense, their theoretical grounds were not sufficiently discussed in the past studies. In our previous work,...
This work implements a type of string kernel called Mismatch kernel, together with a methodology involving Support Vector Machines (SVM) for solving 14 molecular function classification problems of land plants (Embryophyta). The implemented methodology uses metaheuristic bio-inspired algorithms for finding optimal hyperparameters of the SVM, to solve the problem of imbalanced data class weights are...
We present a new approach for humanoid gait generation based on movement primitives learned from optimal and dynamically feasible motion trajectories. As testing platform we consider the humanoid robot HRP-2, so far only in simulation. Training data is generated by solving a set of optimal control problems for a minimum-torque optimality criterion and five different step lengths. As the dynamic robot...
This paper proposes a new incremental learning method for heterogeneous domain adaptation, in which the training data from both source domain and target domains are acquired sequentially, represented by heterogeneous features. Two different projection matrices are learned to map the data from two domains into a discriminative common subspace, where the intra-class samples are closely-related to each...
This paper takes advantage of the “MIT Mobile Device Speaker Verification Corpus” (MIT-MDSVC) availability in order to evaluate the performance of three well known text-independent speaker verification approaches on handheld devices, considering the MIT-MDSVC as a representative corpus designed for robust speaker verification tasks on limited vocabulary and limited amount of training data collected...
The number of application based on Apache Hadoop is increasing dramatically due to the robustness and dynamic features of this system. At the heart of Apache Hadoop, the Hadoop File System (HDFS) provides the reliability, scalability and high availability to computation by applying a static replication strategy. However, because of the characteristics of parallel operations on the application layer,...
The task of predicting the stature of human skeletal remains using bone measurements is an important one in bioarchaeology. Classical attempts to solve this problem mostly consist of linear regression formulas on various bone lengths. In order to improve these results, we propose using locally-weighted regression and radial basis function networks in order to fit the available data better, especially...
Maintaining clearance, or distance from obstacles and sampling efficient enough configurations on the medial axises are a vital component for successful motion planning. Maintaining high clearance often creates safer paths for robots. Having bias for sampling on medial axis also offers higher possibility to find a path in complex environment where the feasible configuration space only occupies a small...
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