• Title, Summary, Keyword: 기계학습

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Analysis of massive data in astronomy (천문학에서의 대용량 자료 분석)

  • Shin, Min-Su
    • The Korean Journal of Applied Statistics
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    • v.29 no.6
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    • pp.1107-1116
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    • 2016
  • Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

Network intrusion detection Model through Hybrid Feature Selection and Data Balancing (Hybrid Feature Selection과 Data Balancing을 통한 네트워크 침입 탐지 모델)

  • Min, Byeongjun;Shin, Dongkyoo;Shin, Dongil
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.526-529
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    • 2020
  • 최근 네트워크 환경에 대한 공격이 급속도로 고도화 및 지능화 되고 있기에, 기존의 시그니처 기반 침입탐지 시스템은 한계점이 명확해지고 있다. 이러한 문제를 해결하기 위해서 기계학습 기반의 침입 탐지 시스템에 대한 연구가 활발히 진행되고 있지만 기계학습을 침입 탐지에 이용하기 위해서는 두 가지 문제에 직면한다. 첫 번째는 실시간 탐지를 위한 학습과 연관된 중요 특징들을 선별하는 문제이며 두 번째는 학습에 사용되는 데이터의 불균형 문제로, 기계학습 알고리즘들은 데이터에 의존적이기에 이러한 문제는 치명적이다. 본 논문에서는 위 제시된 문제들을 해결하기 위해서 Hybrid Feature Selection과 Data Balancing을 통한 심층 신경망 기반의 네트워크 침입 탐지 모델을 제안한다. NSL-KDD 데이터 셋을 통해 학습을 진행하였으며, 평가를 위해 Accuracy, Precision, Recall, F1 Score 지표를 사용하였다. 본 논문에서 제안된 모델은 Random Forest 및 기본 심층 신경망 모델과 비교해 F1 Score를 기준으로 7~9%의 성능 향상을 이루었다.

Automatic Text Categorization Using Hybrid Multiple Model Schemes (하이브리드 다중모델 학습기법을 이용한 자동 문서 분류)

  • 명순희;김인철
    • Journal of the Korean Society for information Management
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    • v.19 no.4
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    • pp.35-51
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    • 2002
  • Inductive learning and classification techniques have been employed in various research and applications that organize textual data to solve the problem of information access. In this study, we develop hybrid model combination methods which incorporate the concepts and techniques for multiple modeling algorithms to improve the accuracy of text classification, and conduct experiments to evaluate the performances of proposed schemes. Boosted stacking, one of the extended stacking schemes proposed in this study yields higher accuracy relative to the conventional model combination methods and single classifiers.

Performance Comparison Analysis of AI Supervised Learning Methods of Tensorflow and Scikit-Learn in the Writing Digit Data (필기숫자 데이터에 대한 텐서플로우와 사이킷런의 인공지능 지도학습 방식의 성능비교 분석)

  • Jo, Jun-Mo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.14 no.4
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    • pp.701-706
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    • 2019
  • The advent of the AI(: Artificial Intelligence) has applied to many industrial and general applications have havingact on our lives these days. Various types of machine learning methods are supported in this field. The supervised learning method of the machine learning has features and targets as an input in the learning process. There are many supervised learning methods as well and their performance varies depends on the characteristics and states of the big data type as an input data. Therefore, in this paper, in order to compare the performance of the various supervised learning method with a specific big data set, the supervised learning methods supported in the Tensorflow and the Sckit-Learn are simulated and analyzed in the Jupyter Notebook environment with python.

A Study on Ontology Generation by Machine Learning in Big Data (빅 데이터에서 기계학습을 통한 온톨로지 생성에 관한 연구)

  • Hwang, Chi-Gon;Yoon, Chang-Pyo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.645-646
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    • 2018
  • Recently, the concept of machine learning has been introduced as a decision making method through data processing. Machine learning uses the results of running based on existing data as a means of decision making. The data generated by the development of technology is vast. This data is called big data. It is important to extract the necessary data from these data. In this paper, we propose a method for extracting related data for constructing an ontology through machine learning. The results of machine learning can be given a relationship from a semantic perspective. it can be added to the ontology to support relationships depending on the needs of the application.

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Incremental Superised Learning based on SVM with Unlabeled Documents (레이블이 없는 문서를 이용한 SVM 기반의 점증적 지도학습)

  • 김수영;조성배
    • Proceedings of the Korean Information Science Society Conference
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    • pp.301-303
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    • 2002
  • 컴퓨터가 널리 보급되고 인터넷이 발전함에 따라 수없이 많은 정보가 디지털 형태로 생산되고 있다. 이러한 정보를 사람이 일일이 가공하고 분류하기에는 한계가 있으므로 자동으로 문서를 분류하고자 하는 연구가 대두되었다. 문서를 자동으로 분류하기 위해 기계학습 방법이 많이 이용되고 있다. 기계학습방법을 이용한 문서분류가 좋은 성능을 내기 위해서는 충분한 양의 학습데이터가 필요하다. 학습데이터를 만들기 위해서는 사람이 일일이 분류해야 하므로, 비용이 많이 든다. 본 논문에서는 적은양의 labeled 데이터로부터 시작하여, 점증적으로 unlabeled 데이터를 학습에 참여시킴으로써, 문서분류의 성능을 높이고자 한다. 실험을 통해 Unlabeled 문서데이터를 사용한 것이 좋은 성능을 보였음을 알 수 있다.

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Improvement of existing machine learning methods of digital signal by changing the step-size (학습률(Step-Size)변화에 따른 디지털 신호의 기계학습 방법 개선)

  • Ji, Sangmin;Park, Jieun
    • Journal of Digital Convergence
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    • v.18 no.2
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    • pp.261-268
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    • 2020
  • Machine learning is achieved by making a cost function from a given digital signal data and optimizing the cost function. The cost function here has local minimums in the cost function depending on the amount of digital signal data and the structure of the neural network. These local minimums make a problem that prevents learning. Among the many ways of solving these methods, our proposed method is to change the learning step-size. Unlike existed methods using the learning rate (step-size) as a fixed constant, the use of multivariate function as the cost function prevent unnecessary machine learning and find the best way to the minimum value. Numerical experiments show that the results of the proposed method improve about 3%(88.8%→91.5%) performance using the proposed method rather than the existed methods.

Strategy of Reinforcement Learning in Artificial Life (인공생명의 연구에 있어서 강화학습의 전략)

  • 심귀보;박창현
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.257-260
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    • 2001
  • 일반적으로 기계학습은 교사신호의 유무에 따라 교사학습과 비교사학습, 그리고 간접교사에 의한 강화학습으로 분류할 수 있다. 강화학습이란 용어는 원래 실험 심리학에서 동물의 학습방법 연구에서 비롯되었으나, 최근에는 공학 특히 인공생명분야에서 뉴럴 네트워크의 학습 알고리즘으로 많은 관심을 끌고 있다. 강화학습은 제어기 또는 에이전트의 행동에 대한 보상을 최대화하는 상태-행동 규칙이나 행동발생 전략을 찾아내는 것이다. 본 논문에서는 최근 많이 연구되고 있는 강화학습의 방법과 연구동향을 소개하고, 특히 인공생명 연구에 있어서 강하학습의 중요성을 역설한다.

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Analysis of Korean Language Parsing System and Speed Improvement of Machine Learning using Feature Module (한국어 의존 관계 분석과 자질 집합 분할을 이용한 기계학습의 성능 개선)

  • Kim, Seong-Jin;Ock, Cheol-Young
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.8
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    • pp.66-74
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    • 2014
  • Recently a variety of study of Korean parsing system is carried out by many software engineers and linguists. The parsing system mainly uses the method of machine learning or symbol processing paradigm. But the parsing system using machine learning has long training time because the data of Korean sentence is very big. And the system shows the limited recognition rate because the data has self error. In this thesis we design system using feature module which can reduce training time and analyze the recognized rate each the number of training sentences and repetition times. The designed system uses the separated modules and sorted table for binary search. We use the refined 36,090 sentences which is extracted by Sejong Corpus. The training time is decreased about three hours and the comparison of recognized rate is the highest as 84.54% when 10,000 sentences is trained 50 times. When all training sentence(32,481) is trained 10 times, the recognition rate is 82.99%. As a result it is more efficient that the system is used the refined data and is repeated the training until it became the steady state.

Machine Learning in Media Industry :Focusing on Content Value Evaluation and Production Development (기계학습의 미디어 산업 적용 :콘텐츠 평가 및 제작 자원을 중심으로)

  • Kwon, Shin-Hye;Park, Kyung-Woo;Chang, Byeng-Chul;Chang, Byeng-Hee
    • The Journal of the Korea Contents Association
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    • v.19 no.7
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    • pp.526-537
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    • 2019
  • This study researched the effect of application systems for media industry by using machine learning method focusing on industrial organization theory. First, for applying the system successfully, formation of sympathy about needs is required. The introduction of machine learning can bring change in each stage of value chain especially, decision making process of investment and production process. In investment side, objective performance prediction data can enhance efficiency, and content diversity can decrease with concentrated investment phenomenon to secured content by the system. In production side, if the system support to make creators decrease simple repeat works, production efficiency will increase.