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A method for revealing and resolving conflicts is presented, especially well applicable for resolution of contradictions. It is shown that agents receive a greater autonomy via a correctly directed or selectable identification and conflict resolution inside the accumulated knowledge. The advantages of the introduced method are presented compared to artificial neural networks (ANN) and other trainable...
Video sensor capabilities and sophistication has improved to the point that they are being utilized in vast and diverse applications. Many such applications are now on the verge of providing too much video information reducing the ability to review, categorize, and process the immense amounts of video. Advancement in other technology areas such as Global Positioning System (GPS) processors and single...
Time series data classification is important in many applications. Learning temporal knowledge in time series data is challenging. In this paper we propose a novel machine learning algorithm, Feature Ensemble (FE), to learn effective subsequences of signal features distributed over time series data streams. Both the FE learning and the FE classification have been applied to an application problem...
In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility...
Many real scenarios in machine learning are non-stationary. These challenges forces to develop new algorithms that are able to deal with changes in the underlying problem to be learnt. These changes can be gradual or abrupt. As the dynamics of the changes can be different, the existing machine learning algorithms exhibit difficulties to cope with them. In this work we propose a new method, that is...
Nowadays, as fuel is an important resource for the whole world, researchers are trying a variety machine learning models for fuel flow prediction in industry, aerospace specifically. Different machine learning models have been applied in different applications. This paper will analyze these applications. Many useful points have been found by comparison of those experimental results.
Emotion recognition is very important for applications of human-computer intelligent interaction. It is always performed on facial or audio information with such method as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning is a hot topic in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method which is based on selective...
Power management for the Hybrid Electric Vehicle (HEV) is a challenging problem because of the dual-power-source nature of HEV design and implementation. In this paper, we present an Intelligent Power Controller, UMD_IPC, trained with a machine learning approach to provide optimal power flow for in-vehicle operations. The UMD_IPC is implemented in a HEV model provided by PSAT simulation environment,...
Ensemble methods represent an approach to combine a set of models, each capable of solving a given task, but which together produce a composite global model whose accuracy and robustness exceeds that of the individual models. Ensembles of neural networks have traditionally been applied to machine learning and pattern recognition but more recently have been applied to forecasting of time series data...
This paper presents a Lempel Ziv Complexity (LZC) based pruning algorithm, called Silent Pruning Algorithm (SPA), for designing artificial neural networks (ANNs). This algorithm prunes hidden neurons during the training process of ANNs according to their ranks computed with LZC. LZC extracts the number of unique patterns in a time sequence as a measure of rank. As a result, it is expected that LZC...
Transfer learning is a new learning paradigm, in which, besides the training data for the targeted learning task, data that are related to the task (often under a different distribution) are also employed to help train a better learner. For example, out-dated data can be used as such related data. In this paper, we propose a new transfer learning framework for training neural network (NN) ensembles...
Voltage instability has recently become a challenging problem for many power system operators. This phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. This paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary...
A method for active power security correction based on BP neural network is presented in this paper. The active power security correction is used to give a minimum regulation of the active power output of generators in order to prevent or alleviate overload of transmission lines and tie line groups. This paper first presents the problem of the traditional method based on sensitivity analysis, and...
This paper presents a review and comparison of the software project cost estimation methods that have emerged with more impact in recent years; Expertise and Machine Learning methods. These methods and models have been selected according to an own criteria focusing onto Analogy estimation models and Case Based Reasoning approaches, assuming that they are widely utilized by researchers and with good...
Machine Learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the context of RoboCup international robotic...
Function Point Analysis (FPA) is one of the most reliable methods for measuring the size of computer software. It is extensively being used as Industry standard for sizing. It is also tremendously useful in estimating projects, managing change in requirements, measuring efficiency and communicating functional requirements. International Standard bodies like International Function Point User Group...
Traditional traffic classification techniques like port-based and payload-based techniques are becoming ineffective owning to more and more Internet applications using dynamic port number and encryption techniques. Therefore, in the past few years, many researches have addressed machine learning-based techniques. Most researches of machine learning-based traffic identification use traffic samples...
The Support Vector Machines (SVM) become popular E-Business data mining tools recently, and the datasets of E-Business are usually large-scale. If Support Vector Machines are trained on large-scale datasets, the training time will be very long and the classifier's accuracy will become lower too. As training a large-scale SVM is equated to solve a large-scale quadratic programming (QP) problem, so...
In Malaysia, the screening coverage for cervical cancer is poor, which was at 2% in 1992, 3.5% in 1995, and at 6.2% in 1996, due to the shortage in pathologist workforce being one of the major cause. Study has been done before to overcome this by developing a diagnosis system based on neural networks, so that diagnosis can be done by an automated system with pathologist-like knowledge. Cell's features...
Reinforcement learning is the problem faced by an agent that must learn behaviour through trial and error interactions with a dynamic environment that lacks the educational examples. Q-learning is one of the most popular algorithms among the reinforcement learning methods. Artificial neural network, as in reinforcement learning, is a sub-entry of machine learning, which can be applied on real frames,...
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