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ML algorithms
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The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC).
14p
vishanshan
27-06-2024
1
1
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Salinity intrusion forecasting is essential and challenging for hydrometeorology, especially in climate change. Employing machine learning (ML) algorithms and conventional forecasting techniques are gaining popularity and providing high performance. This study presents a method to optimize a machine learning model based on the Long Short-Term Memory (LSTM) algorithm for multistep-ahead salinity forecasting (up to 7 days) at Dai Ngai station, Soc Trang province.
11p
dianmotminh02
03-05-2024
2
1
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Artificial intelligence - Lecture 13: Machine learning. This lecture provides students with content including: introduction of machine learning; application examples of ML; key elements of a ML problem; issues in machine learning; types of learning problems;... Please refer to the detailed content of the lecture!
3p
codabach1016
03-05-2024
4
2
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In this paper, the method design controller based on the feedback linearization control (FLC) method with optimal parameter for time response thanks to BAT algorithm for magnetic levitation system (MLS). Feedback linearization controller based on equivalent transformations brings a nonlinear system into linear form, then uses the poles-placement method to find parameters for the linear tracking controller.
17p
viengels
25-08-2023
6
4
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Trong bài viết "Phát triển giải thuật lai có sử dụng học máy để giải bài toán định tuyến xe Vehicle routing problems (VRP)" này sẽ đề xuất một sự kết hợp kỹ thuật Học máy Machine Learning ML với một giải thuật lai để giải quyết bài toán VRP, mà giải thuật lai này có được là sự phối hợp giữa giải thuật Tối ưu bầy đàn PSO và giải thuật Di truyền Genetic Algorithm GA.
12p
phuong3128
23-06-2023
8
5
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Accurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings.
15p
vighostrider
25-05-2023
5
2
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In this paper, we have set up an approach to detect botnet of IoT devices using three one-class classi¯er ML algorithms. The algorithms are: one-class support vector machine (OCSVM), elliptic envelope (EE), and local outlier factor (LOF).
20p
redemption
20-12-2021
20
1
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Lecture Advanced Computer Networks - Chapter 12: Machine Learning for Networking. After studying this section will help you understand: final bonus assignment signup; ML algorithms are used in networking problems and how to apply reinforcement learning to decision making in networking problems,...
42p
bachdangky
31-08-2021
5
2
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Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.
12p
vioklahoma2711
19-11-2020
12
2
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The data pertaining to 623 purebred and 202 Fec-B gene introgressed Kashmir Merino lambs born to 25 and 11 sires, respectively were analyzed with the Mixed Model Least Squares and Maximum Likelihood algorithms, PC-2 version computer programme (Harvey. 1990) to assess the effect of Fec-B gene introgression and some non-genetic factors on performance traits of Kashmir Merino sheep.
6p
cothumenhmong3
22-02-2020
24
2
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In the present study, Deep Learning (DL) algorithm or Deep Neural Networks (DNN), one of the most powerful techniques in Machine Learning (ML), is employed for estimation of ultimate load factor of nonlinear inelastic steel truss. Datasets consisting of training and test data are created based on advanced analysis. In datasets, input data are the member cross-sections of the truss members and output data is the ultimate load factor of the whole structure. An example of a planar 39-bar steel truss is studied to demonstrate the efficiency and accuracy of the DL method.
11p
elandorr
05-12-2019
20
0
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Non-sentential utterances (e.g., shortanswers as in “Who came to the party?”— “Peter.”) are pervasive in dialogue. As with other forms of ellipsis, the elided material is typically present in the context (e.g., the question that a short answer answers). We present a machine learning approach to the novel task of identifying fragments and their antecedents in multiparty dialogue. We compare the performance of several learning algorithms, using a mixture of structural and lexical features, and show that the task of identifying antecedents given a fragment can be learnt successfully (f (0.
8p
bunbo_1
17-04-2013
46
2
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In recent years, machine learning (ML) has been used more and more to solve complex tasks in different disciplines, ranging from Data Mining to Information Retrieval or Natural Language Processing (NLP). These tasks often require the processing of structured input, e.g., the ability to extract salient features from syntactic/semantic structures is critical to many NLP systems. Mapping such structured data into explicit feature vectors for ML algorithms requires large expertise, intuition and deep knowledge about the target linguistic phenomena.
1p
nghetay_1
07-04-2013
31
1
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Given the new unusual and usual event models, both adapted from the general usual event model, the HMM topology is changed with one more state. Hence the cur- rent HMM has 2 states, one representing the usual events and one representing the first detected unusual event. The Viterbi algorithm is then used to find the best possible state sequence which could have emitted the observation sequence, according to the maximum likelihood (ML) cri- terion (Figure 2, step 3). Transition points, which define new segments, are detected using the current HMM topol- ogy and parameters.
10p
nhacsihuytuan
06-04-2013
59
3
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Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.
97p
hoa_can
26-01-2013
65
10
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Synthesis of Synchronization Algorithms In this chapter we derive maximum-likelihood (ML) synchronization algorithms for time and phase. Frequency estimation and synchronization will be treated in Chapter 8. The algorithms are obtained as the solution to a mathematical optimization problem. The performance criterion we choose is the ML criterion. In analogy to filter design we speak of synthesis of synchronization algorithms to emphasize that we use mathematics to find algorithms - as opposed to analyzing their performance. 5.
53p
khinhkha
30-07-2010
68
9
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