• Title/Summary/Keyword: Instance-based learning

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A Combined Method of Rule Induction Learning and Instance-Based Learning (귀납법칙 학습과 개체위주 학습의 결합방법)

  • Lee, Chang-Hwan
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.9
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    • pp.2299-2308
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    • 1997
  • While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

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Data Reduction for Classification using Entropy-based Partitioning and Center Instances (엔트로피 기반 분할과 중심 인스턴스를 이용한 분류기법의 데이터 감소)

  • Son, Seung-Hyun;Kim, Jae-Yearn
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.29 no.2
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    • pp.13-19
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    • 2006
  • The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

An Effective Framework for Contented-Based Image Retrieval with Multi-Instance Learning Techniques

  • Peng, Yu;Wei, Kun-Juan;Zhang, Da-Li
    • Journal of Ubiquitous Convergence Technology
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    • v.1 no.1
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    • pp.18-22
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    • 2007
  • Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.

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Learning Relational Instance-Based Policies from User Demonstrations (사용자 데모를 이용한 관계적 개체 기반 정책 학습)

  • Park, Chan-Young;Kim, Hyun-Sik;Kim, In-Cheol
    • Journal of KIISE:Software and Applications
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    • v.37 no.5
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    • pp.363-369
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    • 2010
  • Demonstration-based learning has the advantage that a user can easily teach his/her robot new task knowledge just by demonstrating directly how to perform the task. However, many previous demonstration-based learning techniques used a kind of attribute-value vector model to represent their state spaces and policies. Due to the limitation of this model, they suffered from both low efficiency of the learning process and low reusability of the learned policy. In this paper, we present a new demonstration-based learning method, in which the relational model is adopted in place of the attribute-value model. Applying the relational instance-based learning to the training examples extracted from the records of the user demonstrations, the method derives a relational instance-based policy which can be easily utilized for other similar tasks in the same domain. A relational policy maps a context, represented as a pair of (state, goal), to a corresponding action to be executed. In this paper, we give a detail explanation of our demonstration-based relational policy learning method, and then analyze the effectiveness of our learning method through some experiments using a robot simulator.

A Multiple Instance Learning Problem Approach Model to Anomaly Network Intrusion Detection

  • Weon, Ill-Young;Song, Doo-Heon;Ko, Sung-Bum;Lee, Chang-Hoon
    • Journal of Information Processing Systems
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    • v.1 no.1 s.1
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    • pp.14-21
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    • 2005
  • Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

Estimating Farmland Prices Using Distance Metrics and an Ensemble Technique (거리척도와 앙상블 기법을 활용한 지가 추정)

  • Lee, Chang-Ro;Park, Key-Ho
    • Journal of Cadastre & Land InformatiX
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    • v.46 no.2
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    • pp.43-55
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    • 2016
  • This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.

Instance Based Learning Revisited: Feature Weighting and its Applications

  • Song Doo-Heon;Lee Chang-Hun
    • Journal of Korea Multimedia Society
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    • v.9 no.6
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    • pp.762-772
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    • 2006
  • Instance based learning algorithm is the best known lazy learner and has been successfully used in many areas such as pattern analysis, medical analysis, bioinformatics and internet applications. However, its feature weighting scheme is too naive that many other extensions are proposed. Our version of IB3 named as eXtended IBL (XIBL) improves feature weighting scheme by backward stepwise regression and its distance function by VDM family that avoids overestimating discrete valued attributes. Also, XIBL adopts leave-one-out as its noise filtering scheme. Experiments with common artificial domains show that XIBL is better than the original IBL in terms of accuracy and noise tolerance. XIBL is applied to two important applications - intrusion detection and spam mail filtering and the results are promising.

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Forward Vehicle Tracking Based on Weighted Multiple Instance Learning Equipped with Particle Filter (파티클 필터를 장착한 가중된 다중 인스턴스학습을 이용한 전방차량 추적)

  • Park, Keunho;Lee, Joonwhoan
    • Journal of the Korean Institute of Intelligent Systems
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    • v.25 no.4
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    • pp.377-385
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    • 2015
  • This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

A Basic Study on the Instance Segmentation with Surveillance Cameras at Construction Sties using Deep Learning based Computer Vision (건설 현장 CCTV 영상에서 딥러닝을 이용한 사물 인식 기초 연구)

  • Kang, Kyung-Su;Cho, Young-Woon;Ryu, Han-Guk
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2020.11a
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    • pp.55-56
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    • 2020
  • The construction industry has the highest occupational fatality and injury rates related to accidents of any industry. Accordingly, safety managers closely monitor to prevent accidents in real-time by installing surveillance cameras at construction sites. However, due to human cognitive ability limitations, it is impossible to monitor many videos simultaneously, and the fatigue of the person monitoring surveillance cameras is also very high. Thus, to help safety managers monitor work and reduce the occupational accident rate, a study on object recognition in construction sites was conducted through surveillance cameras. In this study, we applied to the instance segmentation to identify the classification and location of objects and extract the size and shape of objects in construction sites. This research considers ways in which deep learning-based computer vision technology can be applied to safety management on a construction site.

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The Implementation of SCORM Based API Broker for U-Learning System (U-러닝 시스템을 위한 SCORM 기반의 API 브로커 구현)

  • Jeong, Hwa-Young
    • Journal of Internet Computing and Services
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    • v.11 no.1
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    • pp.71-76
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    • 2010
  • This research proposed the method for application of SCORM in U-learning system. That is, I proposed the API broker to connect between U-learning and API Instance of RTE that is existing SCORM based learning object interface environment. The API broker operated handling process using request port and response port between SCORM and U-learning server. For efficient operation in each service, this system has learning contents service buffer in API broker.