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检索条件"机构=Dept. Computer Technology and Computer Graphics"

共 25770 条记录，以下是4731-4740 订阅

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Combining Top-down and Bottom-up Search for Unsupervised Induction of Transduction Grammars 7

Combining Top-down and Bottom-up Search for Unsupervised Ind...

引用

7th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2013

作者： Saers, Markus Addanki, Karteek Wu, Dekai Dept. of Computer Science and Engineering Human Language Technology Center Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781937284473

We show that combining both bottom-up rule chunking and top-down rule segmentation search strategies in purely unsupervised learning of phrasal inversion transduction grammars yields significantly better translation accuracy than either strategy alone. Previous approaches have relied on incrementally building larger rules by chunking smaller rules bottomup;we introduce a complementary top-down model that incrementally builds shorter rules by segmenting larger rules. Specifically, we combine iteratively chunked rules from Saers et al. (2012) with our new iteratively segmented rules. These integrate seamlessly because both stay strictly within a pure transduction grammar framework inducing under matching models during both training and testing-instead of decoding under a completely different model architecture than what is assumed during the training phases, which violates an elementary principle of machine learning and statistics. To be able to drive induction top-down, we introduce a minimum description length objective that trades off maximum likelihood against model size. We show empirically that combining the more liberal rule chunking model with a more conservative rule segmentation model results in significantly better translations than either strategy in isolation. ©2013 Association for Computational Linguistics.

关键词： Bacteriophages

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Stock Market Prediction Using Deep Learning

Stock Market Prediction Using Deep Learning

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Johnshaida, S.K. Kumar, G. Shiva Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350379525

This project focuses on leveraging One kind of deep learning model is the Long Short-Term Memory (LS TM) networks., to forecast stock prices by analyzing historical data and technical indicators. The methodology involves comprehensive steps such as data collection, preprocessing, feature selection, model selection (including LSTM networks), training, and evaluation taking advantage of performance indicators such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). It utilizes a dataset comprising historical gold stock prices and trading volume to train and test the LSTM model. Furthermore, Naive Bayes, Support Vector Machine (SVM), Linear Regression, and other machine learning algorithms are examined in the study, along with more complex methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Results reveal diverse accuracy levels across models, with LSTM demonstrating a tendency to overpredict stock prices. Future endeavors aim to refine model performance, investigate ensemble methods, and integrate additional data sources like economic news and sentiment analysis to bolster prediction accuracy, thereby offering investors and analysts enhanced decision-making tools in the dynamic stock market landscape. © 2024 IEEE.

关键词： Convolutional neural networks

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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

引用

2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Mawarnie, Hana Patricia Widodo Nugraheni, Murien Universitas Negeri Jakarta Dept. of Informatics and Computer Jakarta Indonesia Universitas Negeri Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798331511166

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%. © 2024 IEEE.

关键词： Tweets

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A way of Fuzzy Cerebellar Mode Articulation PID controller design

A way of Fuzzy Cerebellar Mode Articulation PID controller d...

引用

International Conference on Information Science and Engineering

作者： Zhou, Yi-Min Fan, Chang-Wei Chen, Jen-Yang Dept. of optical-electrical and computer engineering University of shanghai for science and technology Shanghai China Dept. of electronic engineering Mingchuan University Taiwan

ISBN: (纸本)9781424480968

In this paper, an adaptive robust Fuzzy Cerebellar Model Articulation Proportional-Integral-Derivative controller for nonlinear complicated system is presented. Embedding robust control assures the stability of system, the compensated controller eliminates the noise of environment and the uncertain. Because of the chattering control signal, the optimization and redesign adaptive laws are presented. All of them are based on Lyapunov function such that the stability and error convergence are assured. Finally, an illustrated example of Electronical Driver system is utilized to show the performance of the proposed control scheme. © 2010 IEEE.

关键词： Robust control

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CoEvRBFN: An approach to solving the classification problem with a hybrid cooperative-coevolutive algorithm

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9th International Work-Conference on Artificial Neural Networks, IWANN 2007

作者： Pérez-Godoy, M. Dolores Rivera, Antonio J. Del Jesus, M. José Rojas, Ignacio Dept. of Computer Science University of Jaén Jaén Spain Dept. of Computers Technology and Arquitecture University of Granada Granada Spain

ISBN: (纸本)9783540730064

This paper presents a new cooperative-coevolutive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm promotes a coevolutive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. As credit assignment three quality factors are considered which measure the role of the RBFs in the whole RBFN. In order to calculate the application probability of the coevolutive operators a Fuzzy Rule Base System has been used. The algorithm evaluation with different datasets has shown promising results. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Radial basis function networks

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Learning to classify threaten e-mail

Learning to classify threaten e-mail

引用

2nd Asia International Conference on Modelling and Simulation, AMS 2008

作者： Balamurugan, S. Appavu Alias Rajaram, R. Dept. of Information Technology Thiagarajar College of Engineering Madurai India Dept. of Computer Science and Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9780769531366

In this paper we study supervised classification of e-mails. We consider the task of Threaten E-mail Detection (i.e. email related to terrorism, fraud, etc.). In this supervised learning setting, we investigate the use of Data mining classifiers for automatic threaten, e-mail detection. We show that Decision Tree is a good choice for this task as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as Support Vector Machines, Naive Bayes. In particular, we are interested in detecting fraudulent and possibly criminal activities from such e-mails. © 2008 IEEE.

关键词： Data mining

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FReNG: Federated Optimization by Using Regularized Natural Gradient Descent 22

FReNG: Federated Optimization by Using Regularized Natural G...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Sen, Mrinmay Gayatri, C. Indian Institute of Technology Hyderabad Dept. of Artificial Intelligence Hyderabad India Mahindra University Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350345346

Federated learning (FL) is an important area in machine learning that addresses the challenges of distributed data by collaboratively training a global model. One major issue with FL is the increased communication rounds or FL iterations between the server and the participating data sources or clients, which is caused by the presence of heterogeneous data across all the clients. To address this issue, this paper introduces FReNG, which utilizes regularized natural gradient descent with exponential moving average of the global gradient on the server. The server-based utilization of regularized natural gradient descent and exponential moving average of the global gradient leads to faster convergence of the global model in heterogeneous settings without additional local space and computational complexities, resulting in comparatively fewer communication rounds, while achieving the targeted accuracy from the global model. As FReNG uses regularized natural gradient descent, it can directly compute the update step without calculating and storing the full Fisher information matrix (FIM), which is one of the key advantage of FReNG. Extensive experimental results on image classification tasks using various heterogeneously partitioned datasets show that FReNG outperforms existing state-of-the-art FL algorithms such as FedProx, SCAFFOLD & FedInit and can compete with other state-of-the-art FL algorithms, FedGA, & DONE (with less per FL iteration communication cost & less local time complexity) in terms of reduced communication rounds, while achieving a certain precision of convergence and a targeted test accuracy from the global model. © 2023 IEEE.

关键词： exponential moving average of gradient federated learning increased communication rounds natural gradient descent slow convergence

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A SVM-based model for Chinese functional chunk parsing 5

A SVM-based model for Chinese functional chunk parsing

引用

5th SIGHAN Workshop on Chinese Language Processing, co-located with COLING/ACL 2006

作者： Zhao, Yingze Zhou, Qiang State Key Laboratory of Intelligent Technology and Systems Dept. of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)1932432701

Functional chunks are defined as a series of non-overlapping, non-nested segments of text in a sentence, representing the implicit grammatical relations between the sentence-level predicates and their arguments. Its top-down scheme and complexity of internal constitutions bring in a new challenge for automatic parser. In this paper, a new parsing model is proposed to formulate the complete chunking problem as a series of boundary detection sub tasks. Each of these sub tasks is only in charge of detecting one type of the chunk boundaries. As each sub task could be modeled as a binary classification problem, a lot of machine learning techniques could be applied. In our experiments, we only focus on the subject-predicate (SP) and predicate-object (PO) boundary detection sub tasks. By applying SVM algorithm to these sub tasks, we have achieved the best F-Score of 76.56% and 82.26% respectively. © 2006 Association for Computational Linguistics.

关键词： Syntactics

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Multipurpose watermarking method based on blind vector quantization

Multipurpose watermarking method based on blind vector quant...

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IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Lin, Chih-Hung Li, Yu-Chiang Wu, Ming-Ni Yang, Shun-Sheng Chen, Kuan-Ju Dept. of Computer Science and Information Engineering Southern Taiwan University Taiwan Dept. of Information Management National Taichung Institute of Technology Taiwan

ISBN: (纸本)9780769537627

Many image watermarking methods have been proposed over the last decade;however, most of them only focus single or two purposes, verification of copyright and integrity. This paper proposed a multipurpose watermarking method based on blind vector quantization (BVQ) and there are three purposes, copyright protection, integrity verification, and distinguish between malicious and non-malicious manipulations, are achieved in this method. The codebook doesn't need to be transmitted to receiver, but reconstructed from the received image. The effects of this method are shown in experimental results. © 2009 IEEE.

关键词： Vector quantization

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OBDTL: Under Water Object Classification Using Transfer Learning 4

OBDTL: Under Water Object Classification Using Transfer Lear...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Roy, Sarthak Ghosh, Partha Goto, Takaaki Sen, Mainak Techno India University Computer Sc. & Engg. Dept. West Bengal Kolkata India Academy of Technology Adisaptagram Dept. of Computer Science & Engg. India Toyo University Dept. of Information Sciences and Arts Saitama Japan

ISBN: (纸本)9798350389128

Underwater object classification is a critical task in domains such as marine research, underwater exploration, maintenance of underwater structures and deep-sea oil industries etc. Accurate detection of underwater objects is a task of vital importance for autonomous underwater vehicles (AUVs). Tasks such as obstacle avoidance and autonomous navigation become easier when underwater obstacles can be recognized. A learningbased, robust object detection model capable of processing images at a high rate is beneficial for real-time object detection. This project focuses on developing a robust underwater object classification system utilizing machine learning techniques to classify objects present in underwater imagery. In this paper, we explore the efficacy of transfer learning using VGG16. VGG16 offers valuable pre-trained weights that encapsulate knowledge about fundamental image features. Here, we have trained a convolutional neural network and a VGG-16 based neural network classifier on three datasets and received 84.89% of accuracy with VGG-16 based classifier using transfer learning. © 2024 IEEE.

关键词： Convolutional neural networks

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