• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.105-108
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• 2001

In this paper, the learning algorithm called advanced expanded and truncate algorithm(AETLA) is proposed to training multilayer binary neural network to approximate binary to binary mapping. AETLA used merit of ETL and MTGA learning algorithm. We proposed to new learning algorithm to decrease number of hidden layer. Therefore, learning speed of the proposed AETLA learning algorithm is much faster than other learning algorithm.

• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.421-431
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• 2014

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• 전자공학회논문지B
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• 제32B권1호
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• pp.180-188
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• 1995

A modified Kohonen's self-organizing feature map (SOFM) algorithm which has binary reinforcement function and a constant learning rate is proposed. In contrast to the time-varing adaptaion gain of the original Kohonen's SOFM algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SOFM due to the constant learning rate. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than that of the original SOFM.

• 한국정보전자통신기술학회논문지
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• 제16권3호
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• pp.161-166
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• 2023

최근 바이너리 코드에 대한 신종·변종 해킹이 증가되고 있으며 소스 프로그램에서 악성코드를 탐지하고 공격에 대한 방어 기술의 한계점이 자주 노출되는 상황이다. 바이너리 코드에 대해 머신러닝, 딥러닝 기술을 활용하여 고도화된 소프트웨어 보안 취약점 탐지 기술과 공격에 대한 방어와 대처 능력이 필요하다. 본 논문에서는 바이너리 코드의 실행 경로를 추적(execution trace)하여 동적 오염 정보를 입력한 후 오염 정보를 따른 특징을 기반으로 멀웨어를 그룹핑하는 멀웨어 클러스터링 방법을 제안한다. 멀웨어 취약점 탐지는 3-계층으로 구성한 Few-shot 학습 모델에 적용하여 각 계층의 CPU, GPU에 대해 F1-score를 산출하였다. 학습 과정에서 97~98%의 성능과 테스트 과정에서 80~81% 정도의 탐지 성능을 얻었다.

• 제어로봇시스템학회논문지
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• 제2권2호
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.870-876
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• 1993

This paper presents a binary clustering network (BCN) and a heuristic algorithm to detect pitch for recognition of keywords in continuous speech. In order to classify nonlinear patterns, BCN separates patterns into binary clusters hierarchically and links same patterns at root level by using the supervised learning and the unsupervised learning. BCN has many desirable properties such as flexibility of dynamic structure, high classification accuracy, short learning time, and short recall time. Pitch Detection algorithm is a heuristic model that can solve the difficulties such as scaling invariance, time warping, time-shift invariance, and redundance. This recognition algorithm has shown recognition rates as high as 95% for speaker-dependent as well as multispeaker-dependent tests.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.465-469
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• 1990

This thesis proposes a new scheme for the recognition of presegmented Hangul characters. The proposed approach is rather insensitive to noise and variation by applying 2 dimensional convolution to learning patterns. In this thesis, the hangul recognition neural network is implemented in the basis of this scheme and recognition rate is analyzed in boo cases of learning which are learning by binary patterns and learning by binary patterns and convoluted patterns together.

• 한국정보통신학회논문지
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• 제24권2호
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• pp.219-224
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• 2020

이진 분류(binary classification)는 머신러닝(machine learning) 분야에서 많이 다루어진 주제이다. 게다가 이진 분류는 다중 분류로 쉽게 발전될 수 있는 중요한 분야이다. 머신러닝 방법들을 적용할 때에 전처리(preprocessing)이나 특징 추출(feature extraction)과 같은 작업이 필수적이다. 이는 분류기 성능을 향상시키기 위한 중요한 작업이다. 본 논문에서는 가중된 최소 자승법을 기반으로 새로운 머신러닝 방법을 제안한다. 또한, 특징 변환시킬 수 있는 새로운 가중치 계산 방법을 제안한다. 이를 통해 특징 변환과 동시에 학습을 진행할 수 있는 방법을 제안한다. 본 제안을 다섯 개의 머신러닝 데이터베이스에서 실험을 진행하였으며 이 데이터베이스에서 우수한 성능을 얻을 수 있었다.