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共 585 条记录，以下是141-150 订阅

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Rainfall Prediction Using machine learning 1

Rainfall Prediction Using Machine Learning

引用

1st international Conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

作者： Gupta, Akash Mall, Hitesh Kumar Janarthanan, S. Galgotias University School of Computing Science and Engineering UP Greater Noida India

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Rainfall forecasting is a single of difficult and unpredictable undertakings that has a major influence on human society. Predictions that are accurate and timely can help to avert human and financial loss. This study contains a series of experiments that include the utilisation of basic machine learning techniques to build Weather forecasting models that estimate whether it will rain in major cities tomorrow based on the day's meteorological data. This comparative research looks at three aspects of modelling inputs, modelling methodologies, and preprocessing procedures. The findings demonstrate how different machine learning systems perform on a range of assessment parameters, as well as their capacity to forecast rainfall using weather data analysis. Agriculture is crucial to India's survival. The importance of rainfall in agriculture cannot be overstated. Rainfall forecasting has been a key problem in recent years. Individuals can be more aware of the weather and make more educated judgments by predicting rainfall. © 2022 IEEE.

关键词： Rain

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An empirical comparison of label prediction algorithms on automatically inferred networks

An empirical comparison of label prediction algorithms on au...

引用

1st international Conference on pattern recognition Applications and Methods, ICPRAM 2012

作者： Ali, Omar Zappella, Giovanni De Bie, Tijl Cristianini, Nello Intelligent Systems Laboratory Bristol University Bristol United Kingdom Dipartimento di Matematica'F. Enriques' Universitá Degli Studi di Milano Milan Italy

ISBN: (纸本)9789898425980

The task of predicting the label of a network node, based on the labels of the remaining nodes, is an area of growing interest in machine learning, as various types of data are naturally represented as nodes in a graph. As an increasing number of methods and approaches are proposed to solve this task, the problem of comparing their performance becomes of key importance. In this paper we present an extensive experimental comparison of 15 different methods, on 15 different labelled-networks, as well as releasing all datasets and source code. In addition, we release a further set of networks that were not used in this study (as not all benchmarked methods could manage very large datasets). Besides the release of data, protocols and algorithms, the key contribution of this study is that in each of the 225 combinations we tested, the best performance-both in accuracy and running time-was achieved by the same algorithm: Online Majority Vote. This is also one of the simplest methods to implement.

关键词： data mining

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Automatically Identifying a Software Product's Quality Attributes through Sentiment Analysis of Tweets

Automatically Identifying a Software Product's Quality Attri...

引用

1st international workshop on Natural Language Analysis in Software Engineering (NaturaLiSE)

作者： Dehkharghani, Rahim Yilmaz, Cemal Sabanci Univ Dept Comp Sci & Engn Istanbul Turkey

ISBN: (纸本)9781467362719

Software quality attributes can be identified based on software features such as security, reliability and user-friendliness. This process can be done either manually or automatically. Sentiment analysis refers to the sentiment extraction task from resources such as natural language texts. We study the application of sentiment analysis on extracting the quality attributes of a software product based on the opinions of end-users that have been stated in microblogs such as Twitter. Our findings obtain advantageous techniques such as document frequency of words in a large number of tweets. The extracted results can help software developers know the advantages and disadvantages of their products.

关键词： Sentiment analysis data mining software quality attributes machine learning twitter

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Application of Collaborative Filtering Algorithm based Collaboration and Collaborative Environment in the Inheritance of Traditional Ceramic Culture 24

Application of Collaborative Filtering Algorithm based Colla...

引用

1st international Conference on Image Processing machine learning and pattern recognition

作者： Yang, Shiyu Dankook Univ Yongin South Korea

ISBN: (纸本)9798400707032

Nowadays, the protection and inheritance of traditional culture are facing unprecedented challenges. Against this background, this study selects traditional ceramic culture with profound cultural deposits as the research object, aiming to explore how to utilize modern scientific and technological means, especially information technology, to promote and strengthen the inheritance of traditional culture. We use collaborative filtering algorithms to build a learning environment based on collaboration and cooperation, which analyzes and processes user behavioral data to recommend learners with similar interests or needs to collaborate with each other, thus improving learning efficiency and depth of cultural understanding. In the knowledge test score, it went from 65-70 at the beginning to nearly 80;and in the skill assessment score, it improved from below 75 to about 90. It is found that this approach not only effectively promotes communication and interaction among learners and increases learning motivation, but also enhances learners' understanding and skill mastery of ceramic culture in a relatively short period of time.

关键词： Collaborative Filtering Algorithm Cooperative Environment Traditional Ceramic Cultural Heritage data Training Recommender System

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The evaluation of wavelet and data driven feature selection for image understanding

The evaluation of wavelet and data driven feature selection ...

引用

1st international Conference on Biomedical Engineering and Informatics

作者： Liu Jinshuo Zhang Dengyi Liu Siwen Fang Ying Zhang Ming Wuhan Univ Comp Sch Hubei Peoples R China Urban Construct Sch Jilin Dept Mech Engn Jilin Peoples R China Wuhan Huanpi Kaidi Water Serv Co Ltd Wuhan Peoples R China

ISBN: (纸本)9780769531182

Image mining with pattern recognition methods has been widely used to understand the image knowledge. The general rule for the number of features is that the number can not be too much to improve the efficiency and speed of mining. This paper presents the wavelet methods for feature compression, and gives the evaluation of the wavelet methods comparing with data driven feature selection using the example of the project of image mining of pathogen yeast Cryptococcus Neoformans. The experiments show that the wavelet methods for feature compression are almost as effective as data driven feature selection to identify variance pathogen condition. The experiments are built on the training images set and evaluated using the new test set images with machine learning tool WEKA.

关键词： wavelet image mining data driven feature compression

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Geometric decision rules for instance-based learning problems

引用

1st international Conference on pattern recognition and machine Intelligence

作者： Bhattacharya, B Mukherjee, K Toussaint, G Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada McGill Univ Sch Comp Sci Montreal PQ Canada

ISBN: (纸本)3540305068

In the typical nonparametric approach to classification in instance-based learning and data mining, random data (the training set of patterns) are collected and used to design a decision rule (classifier). One of the most well known such rules is the k-nearest neighbor decision rule (also known as lazy learning) in which an unknown pattern is classified into the majority class among the k-nearest neighbors in the training set. This rule gives low error rates when the training set is large. However, in practice it is desired to store as little of the training data as possible, without sacrificing the performance. It is well known that thinning (condensing) the training set with the Gabriel proximity graph is a viable partial solution to the problem. However, this brings up the problem of efficiently computing the Gabriel graph of large training data sets in high dimensional spaces. In this paper we report on a new approach to the instance-based learning problem. The new approach combines five tools: first, editing the data using Wilson-Gabriel-editing to smooth the decision boundary, second, applying Gabriel-thinning to the edited set, third, filtering this output with the ICF algorithm of Brighton and Mellish, fourth, using the Gabriel-neighbor decision rule to classify new incoming queries, and fifth, using a new data structure that allows the efficient computation of approximate Gabriel graphs in high dimensional spaces. Extensive experiments suggest that our approach is the best on the market.

关键词： learning systems

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Non-hierarchical clustering with rival penalized competitive learning for information retrieval? 1st

Non-hierarchical clustering with rival penalized competitive...

引用

1st international workshop on machine learning and data mining in pattern recognition, MLDM 1999

作者： King, Irwin Lau, Tak-Kan Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong

ISBN: (纸本)3540665994

In large content-based image database applications, efficient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques for text retrieval are well understood, efficient and robust indexing methodology for image retrieval is still in its infancy. In this paper, we present a non-hierarchical clustering scheme for index generation using the Rival Penalized Competitive learning (RPCL) algorithm. RPCL is a stochastic heuristic clustering method which provides good cluster center approximation and is computationally efficient. Using synthetic data as well as real data, we demonstrate the recall and precision performance measurement of nearest-neighbor feature retrieval based on the indexing structure generated by RPCL. © Springer-Verlag Berlin Heidelberg 1999.

关键词： stochastic systems

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Silicon support vector machine with on-line learning

Silicon support vector machine with on-line learning

引用

1st international workshop on pattern recognition with Support Vector machines

作者： Genov, R Chakrabartty, S Cauwenberghs, G Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

Training of support vector machines (SVMs) amounts to solving a quadratic programming problem over the training data. We present a simple on-line SVM training algorithm of complexity approximately linear in the number of training vectors, and linear in the number of support vectors. The algorithm implements an on-line variant of sequential minimum optimization (SMO) that avoids the need for adjusting select pairs of training coefficients by adjusting the bias term along with the coefficient of the currently presented training vector. The coefficient assignment is a function of the margin returned by the SVM classifier prior to assignment, subject to inequality constraints. The training scheme lends efficiently to dedicated SVM hardware for real-time pattern recognition, implemented using resources already provided for run-time operation. Performance gains are illustrated using the Kerneltron, a massively parallel mixed-signal VLSI processor for kernel-based real-time video recognition.

关键词： support vector machine (SVM) large margin classifiers quadratic programming (QP) sequential minimum optimization (SMO) matrix-vector multiplication (MVM) pattern recognition kernel machines analog array processors

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Multiple-Instance learning with Instance Selection via Dominant Sets

引用

1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Erdem, Aykut Erdem, Erkut Hacettepe Univ TR-06800 Ankara Turkey

ISBN: (纸本)9783642244704

Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supervised learning problem via instance selection. The novelty of the proposed approach comes from its selection strategy to identify the most representative examples in the positive and negative training bags, which is based on an effective pairwise clustering algorithm referred to as dominant sets. Experimental results on both standard benchmark data sets and on multi-class image classification problems show that the proposed approach is not only highly competitive with state-of-the-art MIL algorithms but also very robust to outliers and noise.

关键词： Clustering algorithms

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Predictive modeling toward identification of sex from lip prints - machine learning in cheiloscopy 4

Predictive modeling toward identification of sex from lip pr...

引用

Joint of the 4th international Conference on Applied Informatics workshops, ICAIW 2021, 2nd international workshop on Applied Artificial Intelligence, WAAI 2021, 3rd international workshop on Applied Informatics for Economy, Society, and Development, AIESD 2021, 4th international workshop on data Engineering and Analytics, WDEA 2021, 1st international workshop on Knowledge Management and Information Technologies, WKMIT 2021 and 4th international workshop on Smart and Sustainable Cities, WSSC 2021

作者： Sabelli, Agustín F. Chatterjee, Parag Pollo-Cattaneo, Ma Florencia Universidad Tecnológica Nacional Facultad Regional Buenos Aires Buenos Aires Argentina

Cheiloscopy is a forensic investigation technique that deals with identification of humans based on lips traces. Lip traces hold multifarious features and could be analyzed in different ways to identify the links with personal identifying features. machine learning holds strong application in this domain, especially for pattern recognition and further interpretation. This work is focused on a brief survey of existing machine learning approaches in Cheiloscopy. Also, a comparative study of predictive models has been presented based on an original dataset of lip prints where supervised models have been used to predict the biological sex of the persons using their lip traces. machine learning on one hand automatizes the identification process and poses a significant potential in analyzing huge number of lip traces with considerable accuracy. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Image processing

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