• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.258-267
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• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

• Journal of Internet Computing and Services
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• v.7 no.4
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• pp.91-99
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• 2006

The incremental parsing technique plays an important role in language-based environment which allows the incremental construction of a program. It improves the performance of a system by reanalyzing only the changed part of a program. The conventional incremental parsing uses the stack data structure in order to store the parsing information. In this paper, we suggest a threaded tree construction algorithm which parse by adding the threaded node address instead of using a stack data structure. We also suggest an incremental threaded tree construction which has incremental parsing process of five steps using the constructed threaded tree.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.207-211
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• 2023

Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.1
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• pp.33-36
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• 2009

Incremental converters provide a solution for such measurement applications, as they retain most of the advantages of conventional ${\Delta}{\Sigma}$ converters, and yet they are capable of offset-free and accurate conversion. Most of the previous research on incremental converters was for single-channel and dc signal applications, where they can perform extremely accurate data conversion with more than 20-bit resolution. In this paper, a design technique for implementing multi-channel incremental data converters to convert narrow bandwidth ac signals is discussed. It incorporates the operation principle, topology, and digital decimation filter design. The theoretical results are verified by simulation results.

• The Journal of Industrial Distribution & Business
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• v.5 no.1
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• pp.5-9
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• 2014

Purpose - This study aims to examine the incremental information content of cash flw from operations and earnings in the Iranian capital market. Design, methodology, and approach - Based on market-based accounting research, this study uses statistical associations between accounting data (earnings and cash flw) and stock returns to assess/measure the incremental information content (value relevance) of cash flw and earnings. A multivariate regression model based on panel data is used to examine the incremental information content of earnings and cash flow from operations. Results - The results show that both earnings and cash flow from operations have incremental information content beyond each other. These results are consistent with the findings of recent studies. Overall, the fidings of this study support the usefulness of cash flw information in addition to earnings, in fim valuation by investors in the Iranian market. Conclusions - The study makes the following contributions to the Iranian literature on incremental information content of cash flw and earnings. First, this study employs actual cash flw data derived from cash flw statements. Second, this study employs a large sample size for a more recent period.

• 전기의세계
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• v.29 no.6
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• pp.389-392
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• 1980

For Markov graphic source, it is well known that the conditional runlength coding for the runs of correct prediction is optimum for data compression. However, because of the simplicity in counting and the stronger concentration in distrubution, the incremental run is possibly a better parameter for coding than the run itself for some cases. It is shown that the incremental-runlength is also geometrically distributed as the runlength itself. The distribution is explicitly described with the basic parameters defined for a Markov model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.77-82
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• 2020

Most of the navigation in the vehicle has been developed based on a complex structure of PSF(Physical Storage Format) files, making it difficult to support incremental map updates. DB-based navigation is drawing attention as a next-generation navigation method to solve this problem. In DB-based navigation that supports incremental map updates, data fragmentation due to continuous map data updates can increase data access costs, which can lead to a decrease in search performance. In this paper, as one of the performance enhancement methods of DB-based navigation that supports incremental map updates, data fragmentation prevention techniques were presented and the performance improvement effect was verified through actual implementation.

• Journal of KIISE:Software and Applications
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• v.27 no.2
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• pp.97-107
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• 2000

Incremental learning generally reduces training time and increases the generalization of a neural network by selecting training data incrementally during the training. However, the existing methods of incremental learning repeatedly evaluate the importance of training data every time they select additional data. In this paper, an incremental learning algorithm is proposed for pattern classification problems. It evaluates the importance of each piece of data only once before starting the training. The importance of the data depends on how close they are to the decision boundary. The current paper presents an algorithm which orders the data according to their distance to the decision boundary by using clustering. Experimental results of two artificial and real world classification problems show that this proposed incremental learning method significantly reduces the size of the training set without decreasing generalization performance.

• Journal of the Korean Mathematical Society
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• v.57 no.2
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• pp.401-414
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• 2020

It is computationally expensive to compute principal components from scratch at every update or downdate when new data arrive and existing data are truncated from the data matrix frequently. To overcome this limitations, incremental principal component analysis is considered. Specifically, we present a sliding window based efficient incremental principal component computation from a covariance matrix which comprises of two procedures; simultaneous update and downdate of principal components, followed by the rank-one matrix update. Additionally we track the accurate decomposition error and the adaptive numerical rank. Experiments show that the proposed algorithm enables a faster execution speed and no-meaningful decomposition error differences compared to typical incremental principal component analysis algorithms, thereby maintaining a good approximation for the principal components.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.