Learning Methods(학습 방법)란 무엇입니까?
Learning Methods 학습 방법 - The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML). [1] These techniques are classified into descriptive approaches, statistical and stochastic methods, diffusion process based approaches, topological based methods, data mining and learning methods, and approaches based on hybrid content mining. [2] Various crack image samples and learning methods are used for efficiently training the proposed network. [3]Machine Learning Methods
Editorial for “Prediction of High-Risk Cytogenetics Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods” Multiple myeloma (MM) is a plasma cell malignancy that exists at one end of a spectrum of plasma cell disorders. [1] Therefore, the aim of this paper is to investigate and compare the performances of the classical machine learning methods: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and K-means. [2] Models reviewed in this chapter include persistence method, physical models, statistical methods, machine learning methods, and hybrid methods. [3] This thesis develops a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy (using horizontal axis wind turbines), geothermal energy (using very shallow ground source heat pumps) and solar energy (using photovoltaic solar panels over rooftops). [4] This research shows how we can use the former patterns of software vulnerabilities-severity along with machine learning methods to predict the vulnerabilities severity of that software in the future. [5] This approach can be quickly adapted using various mathematical tools and machine learning methods to achieve a given quality probabilistic assessment. [6] In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. [7] Prospects for using machine learning methods in forensics are analyzed. [8] This work deals with the problem of fail detection in a BPM system from event logs, based on machine learning methods. [9] Procedures using two machine learning methods (CNN and SVM) were developed to segment OCT images, respectively. [10] Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label. [11] In this paper, we explore use of machine learning methods for forest inventory, since it has great impact on economic and ecological sustainability. [12] Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. [13] We then use three supervised machine learning methods to identify Android malware traffic. [14] Machine learning methods, specifically, convolutional neural networks (CNNs), have emerged as an integral part of scientific research in many disciplines. [15] Predictive discrete output includes machine learning methods and analyzing heart rate-based fluctuation dissipation theory, and finally, predictive methods with continuous output include time variant method and nonlinear dynamic modeling of heart rate. [16] Traditional machine learning methods extract features manually. [17] We demonstrated that multitask modeling provides significant improvement over single-output models and other machine learning methods. [18] A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. [19] For classification studies, seven machine learning methods along with six molecular fingerprints were applied to develop qualitative classification models. [20] The experimental results show that, when predicting disease from the set of 18 possible diseases with 30 possible symptoms, the proposed technique show better performance than traditional machine learning methods. [21]"자기 공명 영상을 기반으로 한 다발성 골수종의 고위험 세포 유전학 상태 예측: 라디오믹스의 유용성 및 기계 학습 방법 비교"에 대한 사설 다발성 골수종(MM)은 형질 세포 스펙트럼의 한쪽 끝에 존재하는 형질 세포 악성 종양입니다. 장애. [1] nan [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21]
Deep Learning Methods
Editorial for “Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods” Ultrasound (US) is currently the predominant modality for primary assessment of fetal anatomy and maternal conditions during pregnancy; however, fetal magnetic resonance imaging (MRI) has been increasingly used in clinical practice and is considered as a complementary technology, which is not affected by maternal obesity, oligohydramnios, multiple fetuses, fetal position, and bones, and can offer additional anatomical and pathological information that cannot be accurately provided by US. [1] In recent years, Deep Learning methods have become very popular in NLP classification tasks, due to their ability to reach high performances by relying on very simple input representations. [2] Although various methods based on the hand-crafted features and deep learning methods have been developed for various applications in the past few years, distinguishing untrained identities in testing phase still remains a challenging task. [3] Deep learning methods offer a new approach for designing decoders that can be trained and tailored for arbitrary channel statistics. [4] Convolutional neural networks(CNN) are a subset of deep learning methods recently used widely for image segmentation. [5] In practical applications, deep learning methods can release people from feature engineering to a certain extent. [6] The intrinsic value of deep learning methods lies in their ability to generalize. [7] In this paper we evaluate the current state of the art in natural language paraphrase generation using deep learning methods. [8] Deep learning methods have recently made significant contributions to sound event detection. [9] • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners. [10] We apply machine-learning methods, including gradient boosting, multilayer perceptrons, and deep learning methods with long short-term memory units; and consider legal, linguistic, statistical information, and different word embedding methods to build several classifiers. [11] To overcome the limitations of the traditional methods and explore the application of deep learning methods in the field of motion estimation, this study proposes a method to estimate human arm motion using deep learning networks. [12] This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. [13] Recent works have addressed this problem with deep learning methods. [14]"다중 인스턴스 딥 러닝 방법을 사용한 태아 뇌 MRI의 이미지 품질 평가" 편집 초음파(미국)는 현재 임신 중 태아 해부학 및 산모 상태의 1차 평가를 위한 주된 양식입니다. 그러나 태아 자기공명영상(MRI)은 임상 실습에서 점점 더 많이 사용되고 있으며 산모 비만, 양수과소증, 다태자, 태아 위치 및 뼈의 영향을 받지 않고 추가적인 해부학적 및 병리학적 제공을 제공할 수 있는 보완 기술로 간주됩니다. 미국에서 정확하게 제공할 수 없는 정보. [1] nan [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14]