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The music classification techniques can be discriminated into two categories - based by music content-based classification and training by learning machine classification. Both have their advantages and disadvantages. For music content-based classifications, most of the approaches are based on single music feature, such as melody or chord, and the accuracy is up to 70% in few genres of music. However,...
This paper describes an approach used to build and optimize a practical AI solution for a 3D boxing simulation game. The two main features of the designed AI agent are believability (human-likeness of agent's behavior) and effectiveness (agent's capability to reach own goals). We show how learning by observation and case-based reasoning techniques are used to create believable behavior. Then we employ...
The support vector machines (SVMs), as one of special regularization methods, has been used successfully in the field of pattern recognition. However, the traditional SVMs, a supervised learning method, gets the normal vector of the decision boundary mainly according to the largest interval law but has not taken the underlying geometric structure and the discriminant information into full consideration...
In recent years, the effect of globalization environment, and the aging of engineers and skill workers have tremendously changed the manufacture industry, therefore, transfer of technology and skill has become a very important issue. Parallel with the advancement of the Internet, e-learning systems stress on the learning of knowledge, skill transfer is rarely mentioned. In this paper, we propose a...
In order to solve the “labeling bottleneck” problem of short text categorization, a novel Semi-Supervised Expectation-Maximization short text categorization method based on Random Subspace (RS-EM) is used in this paper. RS-EM performs an iterative EM style training where multiple models are trained on subsets by using random subspace method. This combination of the stochastic discrimination theory...
This study explored students' information sorting ability and problem-solving ability on the effect of using classified Social Bookmarking of network knowledge. A quasi-experimental research design was conducted in a medium elementary school in northern Taiwan. The results show there was a positive effect on information sorting ability and problem-solving ability. It also implies that students built...
Conversational Recommender Systems (CRSs) are intelligent E-commerce applications that interactively assist online users by following a default recommendation strategy. Typically, the strategy remains hard-coded during the interaction, thus making it impossible for CRSs to adapt to the dynamic user needs. In a previous paper, we have proposed and validated a novel technology that allows CRSs to autonomously...
This paper introduces a fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal. The experimental results obtained on a spoken Arabic digit dataset confirmed that for the same rate of recognition the proposed method,...
Search engines and information retrieval (IR) systems provide a mechanism for users to access large amounts of information available through the Internet. However, in order to find the desired information, the user has to go through a staggering amount of information retrieved from highly dynamic resources. Experimental results show that the approach proposed for constructing specialized domains improves...
AdaBoost is known as an effective method for improving the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost is prone to overfitting, especially in noisy domains. On the other hand, the k-nearest neighbors (kNN) rule is one of the oldest and simplest methods for pattern classification, when cleverly combined with prior knowledge,...
Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns...
In this brief, a Maximum Generalized Fisher Criterion (MGFM) based on manifold learning is presented. The proposed algorithm integrates both class information and the manifold information with the aim at finding an optimal subspace to maximize a Fisher form, which can characterize the intra-class compactness of the neighboring points with identical class and the inter-class separability of the other...
Advances in speech technology and computing power have created a surge of interest in the practical application of speech recognition. The main goal of this paper is to facilitate recognition accuracy, with emphasis on acoustic and language modeling. With the recognition of speech commands generation of the commands for desktop items activation. Practical speech recognition also requires the computation...
In this paper we focus on building keyword search service over unstructured Peer-to-Peer (P2P) networks. Current state-of-the-art keyword search approaches for unstructured P2P systems are either blind or informed. Blind search methods such as flooding in Gnutella generate a large of redundant cloned messages and waste network bandwidth. Informed approaches such as routing indices can allow nodes...
This paper presents an approach to the single output regression problem using ensemble of duo output neural networks based on bagging technique. Each component in the ensemble consists of a pair of duo output neural networks. The first neural network is trained to provide duo outputs which are a pair of truth and falsity values whereas the second neural network provides a pair of falsity and truth...
Personalized meta-search engine is one search engine that we teach the machine to learn users' interest, so the search engine can help users to pick up the useful information for them quickly by using their interest keeping in the database. Personalized meta-search engine can sort the results according to users' interest, the results that user likes will be the top of the results. It is a good measure...
As an essential stage of human ear recognition, Ear detection has a direct and important impact on final recognition performance. Traditional Adaboost algorithm based human ear detection method has some inherent drawbacks will lead to imperfect ear detection, such as the long time training, overly dependent on ear samples quality, etc. Therefore, to overcome such problems partially, the strategies...
Semantic focused crawler is an important part of semantic vertical search engine. It is receiving increasing attention as a well founded alternative to search web with the problem of locating topical resource on entire web. In order to retrieval documents related to a given topic, in this paper, we propose QBLP Algorithm which enable crawler adaptive with the changing environment. This feature makes...
A prospective buyer interested in a particular item may find out information about the item from various sources, including product reviews. With interactive information sharing facilitated by Web 2.0, a lot of product reviews are available on the web. For a popular item with a large number of reviews, a prospective buyer could use some help in selecting only reviews of interest, such as, only positive...
Developing highly interpretable commonly presents significant challenges to decision support system. In previous research work, partial information had provided poor result in the problem of learning classifiers. The behavior of some learning algorithm may only be explored by uncertainty analyses. We propose a novel information extraction by utilizing fuzzy measure in active learning to focus on the...
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