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This paper describes an adaptive fusion human-machine system supporting an analyst in recognizing threat. The system is designed in the framework of Transferable Belief Model for processing complex unreliable and uncertain data streams coming from multiple sources to improve threat recognition and detect new “unknown” threat. The focus of the paper is on the latter: designing a method of detection...
In this paper we propose a neural net based characters recognition scheme for Bangla printed text books. There are a lot of scientific literature, novels, magazines and books etc that are written in Bangla language. More than 400 million people use Bangla language. Most of the library and educational institutions want to keep copy of the books in a digital format. For storing those books in digital...
The paper proposes a novelty detector based on an artificial neural network forecaster. It shows how such forecaster can be constructed and as a novelty detector. Two variations of the forecaster are presented — one is based on backpropagation, and the other on Rprop. It is shown how the detector can be used to approach the exploration vs. exploitation trade-off. Experimental results are presented...
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...
Considering that expert's demonstrations are usually sub optimal and failed demonstrations often have some useful guidance, in this paper, a Discriminative Apprenticeship Learning algorithm is proposed, where the apprentice is taught with the join of failed attempts to acquire the ability that could discriminate the preference and non-preference cases so that to actively take a corresponding action...
Inverse Reinforcement Learning (IRL) is an approach for domain-reward discovery from demonstration, where an agent mines the reward function of a Markov decision process by observing an expert acting in the domain. In the standard setting, it is assumed that the expert acts (nearly) optimally, and a large number of trajectories, i.e., training examples are available for reward discovery (and consequently,...
Recognition of the status of ball mill load (ML) is very important. In practice, operators keep the ML at optimizing range using experience, which always lead to the mill running in the status of lower-load or over-load. A novel ML recognition approach combined with fast Fourier transform (FFT), kernel principal component analysis (KPCA) and K nearest neighbor (KNN) based shell vibration signal is...
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...
In this paper, a radial basis neural network (RBFN) for lung cancer screening algorithm is presented. Because of the learning characteristics of the radial basis neural network (RBFN), it has been selected to train the samples, which are the lung cancer examples, and then extracts the internal relations between the pathogenic factors and inducing lung cancer, and eventually it generates empirical...
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...
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...
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...
In this article, a new Multi Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) is introduced. It is an improvement of a single OSELM (single agent) by combined multiple OSELMs (multi agents) with a final decision making module (parent agent). Here, the development of the parent agent is motivated by the Bayesian Formalism...
Web automation programs offer a means for users to enhance the usability of the web. These programs can be published on a wiki or other repository, thereby making them available for use by other users. However, in addition to programs of broad usefulness to the community at large, these repositories also contain many programs that are unreliable or highly specialized to the needs of very small sub-...
This paper presents a new learning environment for developers of mobile apps that merges two quite different views of the same topic. Creative design and system engineering are core issues in the development process that are based on diverging principles. This new learning environment aims to address both points of view by not suppressing one of them but trying to benefit from both.
In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the...
High performance biometrics helps in reliably identifying persons for access authorization and other purposes. Iris recognition is very effective in identifying persons due to the iris' unique features and the protection of the iris from the environment and aging. We focus on the design and training of a feed-forward artificial neural network for high-performance iris recognition and investigate the...
K-nearest neighbor (KNN) classification is an instance-based learning algorithm that has shown to be very effective when classifying images described by local features. In this paper, we present a combined unsupervised and supervised classification tree based on local descriptors and the KNN algorithm. The proposed tree outperforms the classification accuracy of the exact KNN algorithm.
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