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A reinforcement learning (RL) agent needs a fair amount of experience to find a near-optimal policy. Transfer learning has been investigated as a means to reduce the amount of experience required. Transfer learning, however, requires another similar reinforcement learning task as a transfer source, which can also be costly in the amount of experience required. In this research, we examine the possible...
This paper proposes a hardware accelerator, named IBE (Intelligence Boost Engine), to process both sensor fusion and machine learning algorithms for the Standing-egg SLH200 sensor hub SoC. The IBE is designed to have both efficiency and flexibility to support various emerging applications for future sensor hub SoCs in addition to the sensor fusion and machine learning algorithm (SVM) which are the...
In this paper, we tackle with indefinite kernels by introducing projection matrix to formulate a positive semidefinite kernel. The projection matrix has a nice property of sharing the same set of eigenvectors with the original kernel. The proposed model can be regarded as a generalized version of spectrum method (denoising method and flipping method) by varying parameter λ. The problem of selecting...
In order to improve the recognition performance and solve the problem of computational complexity caused by the high-dimensional data in human identification, a gait recognition method based on manifold learning is proposed in this paper. Firstly, gait energy image (GEI) of a walking person is abstracted from a gait image sequence. And then discrete wavelet decomposition (DWT) and t-Distributed Stochastic...
In this paper, we propose a differential reward based online learning algorithm for classifying web pages into predefined topics based on minimal text available in the URLs. It is then compared with two baseline methods, i.e., Support Vector Machine (SVM) and a state-of-the-art Reinforcement Learning Algorithm using recall, precision and F-measure scores. We conducted experiments on large scale Open...
Because of great volume of web information, information retrieval process of a search engine is of great importance. For each query of user, the number of queries can reach hundred thousands, whereas a few number of the first results have the chance of being checked by user; therefore, a search engine pays attention to putting relevance results in the first ranks as a necessity. This paper introduces...
Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
This paper elaborates the basic structure of a machine learning system in classifying affective state. There are several techniques in classifying the states depending on the type of input-output dataset. A proper selection of techniques is crucial in determining the success rate of the system prediction. The paper proposes a machine learning technique in classifying affective states of human subjects...
By learning the various character image samples, the automatic and synchronistic generation of new Chinese calligraphy styles is a key problem in the computer artistic simulating. A curve analogy method based on FSVM is proposed which can generate new calligraphy styles with the user's constraining styles'parameters. Firstly, the input character image samples are transferred into a hierarchical stroke...
Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the...
The multicollinearity exists in the interpretive variable of regression model , it often brings inconvenience to social post-evaluation. The ridge regression has advantages than LS method. The support vector machines (SVM) is a novel machine learning tool in data mining. It is based on the structural risk minimization (SRM) principle, which has been shown to be more superior than the traditional empirical...
SVM is a novel type of statistical learning method that has been successfully used in speaker recognition. However, training SVM consumes long computing time and large storage space with all training examples. This paper proposes an improved sparse least-squares support vector machine (LS-SVM) for speaker identification. Firstly KPCA is exploited to reduce the dimension of input vectors and to denoise...
In this paper we present a SVM-based method for automatic quality control of a road database in urban areas. The road verification is carried out by comparing the database objects to high-resolution aerial imagery. The method is trimmed to produce reliable results even if the training data selection is partly non-epresentative. A reliability metric is assigned to the SVM decision that is based on...
Sudden Cardiac Death (SCD) is an unexpected death caused by loss of heart function when the electrical impulses fired from the ventricles become irregular. Most common SCDs are caused by cardiac arrhythmias and coronary heart disease. They are mainly due to Acute Myocardial Infarction (AMI), myocardial ischaemia and cardiac arrhythmia. This paper aims at automating the recognition of ST-segment deviations...
RFID localization is a promising new field of work that is eagerly awaited for many different types of applications. For use in a medical context, special requirements and limitations must be taken into account, especially regarding accuracy, reliability and operating range. In this paper we present an experimental setup for a medical navigation system based on RFID. For this we applied a machine...
This paper proposes to enhance the existing methods of Self-Supervised Learning (SSL) with application to autonomous navigation systems through efficient computational approaches that are the principal requirements in a practical system. First, confidence-based auto labeling for self-supervised learning is introduced which identifies and eliminates the input samples with low confidence level that...
Support Vector Machines (SVMs) are used to discover method-specific compilation strategies in Testarossa, a commercial Just-in-Time (JiT) compiler employed in the IBM® J9 Java™ Virtual Machine. The learning process explores a large number of different compilation strategies to generate the data needed for training models. The trained machine-learned model is integrated with the compiler to predict...
Aiming to the difficulties resulted by implicit performance function and the limitations of traditional methods for reliability analysis of underground cavern, a new method based on Gaussian process classification (GPC), which is a newly developed machine learning method, is proposed for structural reliability analysis of underground cavern. The implicit performance function of underground cavern...
Breast Cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Woman within the age of 40-69 have more risk of breast cancer. Though breast cancer leads to death, early detection of breast cancer can increase the survival rate. Clustered Microcalcification (MC) in mammograms is the major indication for early detection of breast cancer. MC is quiet...
Support vector regression (SVR) is a common learning method for machines which is developed these years. Comparing with the other regression models, this method has the advantages of structural risk minimization and strong generalization ability. It is widely used in the prediction field and acquires good effects. The training characters of SVR model are very important to SVR. To solve the problem,...
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