• The Journal of Information Technology
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• v.1 no.1
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• pp.65-84
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• 1998

The MBR (Memory Based Reasoning) is a supervised learning method that utilizes the distances among the input and trained patterns in its classification, and is also called a distance based learning algorithm. The MBR is based on the k-NN classifier, in which teaming is performed by simply storing training patterns in the memory without any further processing. This paper proposes a new learning algorithm which is more efficient than the traditional k-NN classifier and has incremental learning capability, Furthermore, our proposed algorithm is insensitive to noisy patterns, and guarantees more efficient memory usage.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.595-601
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• 2003

In this paper we propose an on line training method for classification based on least squares support vector machine. Proposed method enables the computation cost to be reduced and the training to be peformed incrementally, With the incremental formulation of an inverse matrix in optimization problem, current information and new input data can be used for building the new inverse matrix for the estimation of the optimal bias and Lagrange multipliers, so the large scale matrix inversion operation can be avoided. Numerical examples are included which indicate the performance of proposed algorithm.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• ETRI Journal
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• v.43 no.4
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• pp.630-639
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• 2021

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.266-274
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• 2003

This paper considers an incremental support vector learning for the abnormality detection problems. One of the most well-known support vector learning methods for abnormality detection is the so-called SVDD(support vector data description), which seeks the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to modify the SVDD into the direction of utilizing the relation between the optimal solution and incrementally given training data. After a thorough review about the original SVDD method, this paper establishes an incremental method for finding the optimal solution based on certain observations on the Lagrange dual problems. The applicability of the presented incremental method is illustrated via a design example.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.103-133
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• 2007

The purpose of this study is to investigate whether customer satisfaction is affected by the expenditure for the customer satisfaction such as advertising, promotion, and training. This study also investigate whether the financial performance of the firm is affected by the customer satisfaction. The major findings are summarized as following. First, the customer satisfaction is affected by the expenditure for the customer satisfaction such as promotion, training. But customer satisfaction is not affected by advertising cost. Second, considering the time-lag and incremental valiables, the relationship between customer satisfaction and expenditure for the customer satisfaction is not founded. Third, the customer satisfaction affects positively on the corporate financial performance, such as ratio of operating income to sales, ratio of net income to sales, return on total assets, and return on equity. Finally, considering the time-lag the relationship between customer satisfaction and financial performance is not founded. Considering the incremental valiables, the relationship between customer satisfaction and financial performance is founded when ratio of operating income to sales and return on total assets are used financial performance. These findings imply that the expenditure for promotiom and training is needed to increase the customer satisfaction. Also improvement customer satisfaction is needed to increase the financial performance. The limitations of this study are as following. First, this study could not consider the other variables that would affect on the relationship among expenditure for customer satisfaction, level of customer satisfaction, and financial performance. Second, the results of this study are difficult to generalize because this study is focused on the service industry.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.847-856
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• 2013

We propose a modified learning process for generalized neural network using a learning algorithm by Liu et al. (2001). We consider the effect of initial weights, training results and learning errors using a modified learning process. We employ an incremental training procedure where training patterns are learned systematically. Our algorithm starts with a single training pattern and a single hidden layer neuron. During the course of neural network training, we try to escape from the local minimum by using a weight scaling technique. We allow the network to grow by adding a hidden layer neuron only after several consecutive failed attempts to escape from a local minimum. Our optimization procedure tends to make the network reach the error tolerance with no or little training after the addition of a hidden layer neuron. Simulation results with suitable initial weights indicate that the present constructive algorithm can obtain neural networks very close to minimal structures and that convergence to a solution in neural network training can be guaranteed. We tested these algorithms extensively with small training sets.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.187-196
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• 1996

This paper introduces the concept of fixed weights and proposes an algorithm for classification by adding this concept to vector space separation method in LVQ. The proposed algorithm is based on competitive learning. It uses fixed weightsfor generality and fast adaptation efficient radius for new weight creation, and L1 distance for fast calcualtion. It can be applied to many fields requiring adaptive learning with the support of generality, real-tiem processing and sufficient training effect using smaller data set. Recognition rate of over 98% for the train set and 94% for the test set was obtained by applying the suggested algorithm to on-line handwritten recognition.

• Physical Therapy Rehabilitation Science
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• v.3 no.2
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• pp.112-118
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• 2014

Objective: The purpose of this study was to investigate the effects of an exercise with and without neuromuscular electrical stimulation (NMES) of the quadriceps femoris muscle, on strength, pain, and weight distribution in patients with knee instability post surgery. Design: Randomized controlled trial. Methods: Twenty patients in the early stage of rehabilitation after knee surgery were recruited as subjects and were randomly divided into either experimental group (exercise combined with NMES) (n=10) or control group (n=10). Both groups received strength training of the lower limb for 20 min/day, 5 days/week for 4 weeks. The experimental group used NMES for unilateral quadriceps femoris training with incremental increases in the intensity of isometric contraction over 4 weeks. Outcome measurements were assessed using the digital manual muscle testing, 30-chair stand test (30CST), numeric pain rating scale (NPRS) and weight distribution using the foot analyzer before and after 4 weeks of training. Results: After the 4-week intervention, knee extensor strength increased significantly in the experimental group post intervention (p<0.05), and there was a significant improvement in the experimental group compared with the control group (p<0.05). The 30CST and NPRS scores improved significantly in the experimental group compared to the control group (p<0.05), and there was a significant difference between the two groups (p<0.05). Weight distribution was significantly improved in the experimental group compared with the control group, (p<0.05), but there was no significant difference in improvement between the two groups. Conclusions: This study showed that NMES combined with strengthening exercises of the lower limbs is effective in improving lower limb pain and strength in patients with instability after knee surgery.