• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.469-470
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• 2015

우리는 그룹핑 k-최근접 (Grouping k Nearest Neighbor; GkNN)질의를 지원하기 위하여 유연한 최근접객체(Flexible Nearest Neighbor; FNN)검색 방법을 제안한다. GkNN이란 기존에 제안된 kNN과 다르게 질의자가 요청한 k개의 객체를 모두 확인한 후에 이동 경로의 총합이 가장 작은 k개의 객체를 검색하는 방법이다. 기존 연구에서 제안된 최근접 객체들 (Nearest Neighborhood; NNH) 또한 이 문제를 해결하기 위하여 제안되었다. 그러나 NNH의 문제점은 객체 k와 p가 고정되어 있기 때문에 이동 환경에서 q에서 C까지의 거리가 증가하는 것이다. FNN의 환경은 NNH의 환경과 유사하다. 우리는 NNH의 q에서 집합 C 중 거리 중 가장 짧은 $c_i$ 선택한 후 q에서 $c_i$에 포함된 객체들 모두 검색하는 이동 경로의 총합과 FNN의 이동경로의 총 합을 비교하여 NNH의 문제점을 해결하였다.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.355-365
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• 2004

Kim et al.. (2003) developed an Economic Instability Index (EII) by using mean squared error (MSE) from the neural network (NN) trained on the 1995 KOSPI. In this paper we study validity of the NN. For this we compare the NN with the well known Box-Jenkins linear auto-regressive processes. Our conclusive understanding of the problem is that the NN provides quite effective EII because it tends to overfit.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.418-434
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• 2018

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

• Journal of Korea Game Society
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• v.9 no.2
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• pp.115-123
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• 2009

This paper proposes an algorithm to improve the performance of the spatial partitioning method for flocking behaviors. The core concept is to improve the performance by using the fact that even if a moving entity, boid in flock continuously changes its direction and position, its k-nearest neighbors, kNN to effect on decision of the next direction is not changed frequently. From the previous kNN, the method to check whether new kNN is changed or not is proposed in this paper and then the correctness of the proposed method is proved by two theorems. The proposed algorithm was implemented and its performance was compared with the conventional spatial partitioning method. The results of the comparison show that the proposed algorithm outperforms the conventional one by about 30% with respect to the number of frames per a second.

• Computers and Concrete
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• v.12 no.2
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• pp.229-241
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• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• Journal of the Korean Geotechnical Society
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• v.21 no.9
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• pp.25-34
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• 2005

This paper proposes a selection methodology composed of neural network (NN) and genetic algorithm (GA) to design optimal NN structure. We combine the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications and increase the precision of NN' prediction in the design of NN structure. Genetic selection approach of design parameters of NN is introduced to obtain optimal NN structure. Analyzed results for geotechnical problems are given to evaluate the performance of the proposed hybrid methodology.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1849-1857
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• 1999

We proposed the RPA (Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. This algorithm recursively partitions the pattern space until each hyperrectangle contains only those patterns of the same class, then it computes the average values of patterns in each hyperrectangle to extract a representative. Also we have used the mutual information between the features and classes as weights for features to improve the classification performance. The proposed algorithm used 30~90% of memory space that is needed in the k-NN (k-Nearest Neighbors) classifier, and showed a comparable classification performance to the k-NN. Also, by reducing the number of stored patterns, it showed an excellent result in terms of classification time when we compare it to the k-NN.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1647-1650
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• 2010

다양한 옥내 측위 방법이 연구 발표되었다. 그 중에서 무선근거리통신망을 이용하는 방법은 측위를 위한 별도의 특수 장비를 요구하지 않기 때문에 실용적이다. 무선근거리통신망 기반 옥내 측위에서는 지문방식과 신호세기를 거리로 환산하여 사용하는 방법이 가장 흔히 사용되는 두 가지 방법이다. 지문방식은 시간과 노력이 많이 소요되는 준비단계가 필요하지만 신호세기를 거리로 환산하여 사용하는 방법보다 정확한 반면, 신호세기를 거리로 환산하여 사용하는 방법은 구현하기가 용이하지만 오차가 심하다. 때때로 지문방식 조차도 실제 응용에 적용되기에 부적절할 만큼 오차가 커, [1]은 일종의 지문방식인 K-NN (K nearest neighbors) 방법의 오차를 개선하기 위한 파티클 필터를 소개하였다. 칼만 필터도 역시 K-NN 옥내 측위의 정확도 개선을 위하여 사용된 바 있다. 본 논문은 K-NN 방법의 정확도 개선에 있어 칼만 필터와 파티클 필터의 성능을 비교하는 실험 결과를 소개한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.956-961
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• 2006

Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including k-NN (k-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of k-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either k-NN or Bayesian method according to the number of keystroke samples accumulated by each user.

• Proceedings of the KIPE Conference
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• 2003.11a
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• pp.182-185
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• 2003

This paper combines the adaption of MRAS with the ability of NN for better modeling of nonlinear systen It presents an MRAS using an NN in the adaption mechanism. The technique is applied to a IPMSM drive. The torque constant and stator resistance variations on the speed and position estimations over a wide speed range has been studied. The NN estimators are able to track the varying parameter of different speeds with consistent performance. The validity of the proposed estimator is confirmed by the operating characteristics controlled by neural networks control.