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共 585 条记录，以下是181-190 订阅

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Applying machine learning Technology for Weather Forecasting: A Case study of the Logistic Regression Model 24

Applying Machine Learning Technology for Weather Forecasting...

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

作者： Shen, Kaiwei Beijing Inst Petrochem Technol China Big Data Management & Applicat Beijing Peoples R China

ISBN: (纸本)9798400707032

This study is focused on improving the dependability and precision of weather forecasting by employing the capabilities of Artificial Intelligence. Specifically, this study utilizes Logistic Regression and machine learning techniques to forecast weather, demonstrating the potential in optimizing weather-related activities and disaster management strategies. The study relies on comprehensive weather data observed over several years, sourced from Kaggle, and handles missing data and outliers during its pre-processing stages. The primary machine learning tool applied is Logistic Regression, followed by a stepwise feature selection to identify influential features for accurate weather prediction. The workflow also involves data collection, pre-processing, model building, training, and testing, with provisions for handling both numeric and categorical features along with imputations. The accuracy, precision, and recall of the prediction module are tested using appropriate statistical tools. The Logistic Regression model, upon implementation, demonstrated considerable accuracy, with an ability to predict rainy days and non-rainy days efficiently. An analytical approach was used to examine the model's sensitivity towards the removal of each feature, thereby ascertaining the relative importance of each. Critical predictors like 'Rainfall', 'Pressure9am', and 'WindGustSpeed' exhibited significant effects on the probability of rain. Overall, the use of Logistic Regression and machine learning techniques notably improved rain prediction, offering potential for further advancements in the field of weather forecasting.

关键词： Artificial intelligence data preprocessing Logistic regression model Weather forecasting

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1st international workshop on machine learning, Optimization, and Big data, MOD 2015

1st International Workshop on Machine Learning, Optimization...

引用

1st international workshop on machine learning, Optimization, and Big data, MOD 2015

ISBN: (纸本)9783319279251

The proceedings contain 32 papers. The special focus in this conference is on machine learning, Optimization, and Big data. The topics include: Automatic tuning of algorithms through sensitivity minimization;step down and step up statistical procedures for stock selection with sharp ratio;differentiating the multipoint expected improvement for optimal batch design;dynamic detection of transportation modes using keypoint prediction;effect of the dynamic topology on the performance of pso-2s algorithm for continuous optimization;heuristic for site-dependent truck and trailer routing problem with soft and hard time windows and split deliveries;cross-domain matrix factorization for multiple implicit-feedback domains;advanced metamodeling techniques applied to multidimensional applications with piecewise responses;alternating direction method of multipliers for regularized multiclass support vector machines;a single-facility manifold location routing problem with an application to supply chain management and robotics;an efficient many-core implementation for semi-supervised support vector machines;intent recognition in a simulated maritime multi-agent domain;an adaptive classification framework for unsupervised model updating in nonstationary environments;global optimization with sparse and local Gaussian process models;condense mixed convexity and optimization with an application in data service optimization;soc-based pattern recognition systems for non destructive testing;an efficient numerical approximation for the Monge-Kantorovich mass transfer problem;outlier detection in COX proportional hazards models based on the concordance c-index and data clustering by particle swarm optimization with the focal particles.

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learning from Partially Labeled Sequences for Behavioral Signal Annotation 7th

Learning from Partially Labeled Sequences for Behavioral Sig...

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7th international workshop on machine learning and data mining for Sports Analytics (MLSA)

作者： Aniszewska-stepien, Anna Herault, Romain Hacques, Guillaume Seifert, Ludovic Gasso, Gilles INSA Rouen Normandy St Etienne Du Rouvray France Rouen Univ Normandy Mont St Aignan France

ISBN: (纸本)9783030649111;9783030649128

Herewith, we present a learning procedure that allows to deal with a partially labeled sequence dataset, i.e. when each sequence in the train dataset may contain labeled as well as unlabeled chunks. In our application case, this occurs when motor activity has been manually annotated (due to the recognition based on the video recording) and independently registered by the measuring system of high precision (touch sensors): human annotation misses some events that have been captured by the sensors. In the general setting, we aim at predicting the labels for a new fully unlabeled movement sequence, while the training has been performed on the partially labeled dataset. For this purpose we propose to use classical sequence model (hidden Markov model) that is furnished with a constrained Viterbi algorithm, which gives us a quick access to the hard approximation of the correct labeling sequences. We demonstrate, that this simple modification that constrained Viterbi provide, allows the HMM model to be trained on sparse data, and overall results in surprisingly high log-likelihood and accuracy level in annotating the partially labeled behavioral sequences in climbing. The same time we show the way to access correct labeling of the unannotated signal that can be helpful in various sport science studies for movement pattern sequential prediction.

关键词： Partially labeled data Hidden Markov model Constrained viterbi algorithm Behavioral signal labeling Climbing pattern discovery

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ASM 2015 - Proceedings of the 1st international workshop on Affect and Sentiment in Multimedia, co-located with ACM MM 2015

ASM 2015 - Proceedings of the 1st International Workshop on ...

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1st international workshop on Affect and Sentiment in Multimedia, ASM 2015

ISBN: (纸本)9781450337502

The proceedings contain 10 papers. The topics discussed include: learning combinations of multiple feature representations for music emotion prediction;twitter: a new online source of automatically tagged data for conversational speech emotion recognition;affect recognition in a realistic movie dataset using a hierarchical approach;do others perceive you as you want them to? modeling personality based on selfies;aesthetic photo enhancement using machine learning and case-based reasoning;prediction of user ratings of oral presentations using label relations;continuous arousal self-assessments validation using real-time physiological responses;an interactive system based on yes-no questions for affective image retrieval;and diving deep into sentiment: understanding fine-tuned CNNs for visual sentiment prediction.

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The Epistle to Cangrande Through the Lens of Computational Authorship Verification 2nd

The Epistle to Cangrande Through the Lens of Computational A...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Corbara, Silvia Moreo, Alejandro Sebastiani, Fabrizio Tavoni, Mirko Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa56124 Italy Dipartimento di Filologia Letteratura e Linguistica Università di Pisa Pisa56126 Italy

ISBN: (纸本)9783030307530

The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open. © 2019, Springer Nature Switzerland AG.

关键词： machine learning

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Predicting signal peptides with support vector machines 1st

Predicting signal peptides with support vector machines

引用

1st international workshop on pattern recognition with Support Vector machines

作者： Mukherjee, N Mukherjee, S MIT Ctr Biol & Computat Learning Cambridge MA 02139 USA MIT Whitehead Inst Biomed Res Ctr Genome Res Cambridge MA 02139 USA Univ Calif San Diego Dept Biol La Jolla CA 92093 USA

ISBN: (纸本)354044016X

We examine using a Support Vector machine to predict secretory signal peptides. We predict signal peptides for both prokaryotic and eukaryotic signal organisms. Signalling peptides versus non-signaling peptides as well as cleavage sites were predicted from a sequence of amino acids. Two types of kernels (each corresponding to different metrics) were used: hamming distance, a distance based upon the percent accepted mutation (PAM) score trained on the same signal peptide data.

关键词： Support vector machines

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Enabling Edge Devices that Learn from Each Other: Cross Modal Training for Activity recognition 1

Enabling Edge Devices that Learn from Each Other: Cross Moda...

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1st ACM internationalworkshop on Edge Systems, Analytics and Networking (EdgeSys)

作者： Xing, Tianwei Sandha, Sandeep Singh Balaji, Bharathan Chakraborty, Supriyo Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90024 USA IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781450358378

Edge devices rely extensively on machine learning for intelligent inferences and pattern matching. However, edge devices use a multitude of sensing modalities and are exposed to wide ranging contexts. It is difficult to develop separate machine learning models for each scenario as manual labeling is not scalable. To reduce the amount of labeled data and to speed up the training process, we propose to transfer knowledge between edge devices by using unlabeled data. Our approach, called RecycleML, uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities. Using human activity recognition as a case study, over our collected CMActivity dataset, we observe that RecycleML reduces the amount of required labeled data by at least 90% and speeds up the training process by up to 50 times in comparison to training the edge device from scratch.

关键词： edge devices transfer learning cross modality shared latent representation activity recognition

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Technology of text mining

引用

2nd international workshop on machine learning and data mining in pattern recognition

作者： Visa, A Tampere Univ Technol FIN-33101 Tampere Finland

ISBN: (纸本)3540423591

A large amount of information is stored in databases, in intranets or in Internet. This information is organised in documents or in text documents. The difference depends on the fact if pictures, tables, figures, and formulas are included or not. The common problem is to find the desired piece of information, a trend, or an undiscovered pattern from these sources. The problem is not a new one. Traditionally the problem has been considered under the title of information seeking, this means the science how to find a book in the library. Traditionally the problem has been solved either by classifying and accessing documents by Dewey Decimal Classification system or by giving a number of characteristic keywords. The problem is that nowadays there axe lots of unclassified documents in company databases and in intranet or in Internet. First one defines some terms. Text filtering means an information seeking process in which documents are selected from a dynamic text stream. Text mining is a process of analysing text to extract information from it for particular purposes. Text categorisation means the process of clustering similar documents from a large document set. All these terms have a certain degree of overlapping. Text mining, also know as document information mining, text data mining, or knowledge discovery in textual databases is an merging technology for analysing large collections of unstructured documents for the purposes of extracting interesting and non-trivial patterns or knowledge. Typical subproblems that have been solved axe language identification, feature selection/extraction, clustering, natural language processing, summarisation, categorisation, search, indexing, and visualisation. These subproblems are discussed in detail and the most common approaches axe given. Finally some examples of current uses of text mining are given and some potential application areas are mentioned.

关键词： data mining

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A Methodology for Assessing Carbon Emission Indicators Based on Incorporates machine learning and Multi-modal data 24

A Methodology for Assessing Carbon Emission Indicators Based...

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

作者： Zhang, Shuang Liu, Jia Ma, Rui Sha, Jiangbo Kang, Wenni Qu, Fanghao Kang, Yibin Ningxia Elect Power Res Inst Yinchuan Ningxia Peoples R China State Grid Xintong Yili Technol Co Ltd Nanjing Peoples R China

ISBN: (纸本)9798400707032

Since the status of carbon emission indicators involves many factors, there is often a problem of large errors when evaluating them. For this reason, this paper proposes a study on a carbon emission indicator evaluation method that integrates machine learning and multi-modal data. After comprehensively analyzing the influencing factors of the value of carbon emission rights from the four perspectives of macroeconomics, energy prices, climate change, policy, and the international carbon market, we integrated multi-modal influencing factor data, calculated the corresponding carbon emission costs, and combined them as the input characteristic parameter of the BP neural network, through forward propagation and backward propagation, the linear relationship between it and the value of carbon emission rights is determined, and the corresponding model is constructed to realize the value evaluation of carbon emission rights. In the test results, the fit between the carbon emission rights value assessment results and the actual transaction price has always remained relatively stable, and the specific error has always been within 0.70 yuan/ton, with the maximum error being only 0.66 yuan/ton. Compared with the control group, it has obvious advantages in assessment accuracy and effectiveness respectively.

关键词： Fusion machine learning Multi-modal data Carbon emission indicators Carbon emission costs BP neural network Linear relationship

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An Improvement of Two-dimensional Robust Principal Component Analysis Based on ADMM Solution 24

An Improvement of Two-dimensional Robust Principal Component...

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

作者： Pan, Wen Guo, Yuzhen Nanjing Univ Aeronaut & Astronaut Coll Math Nanjing Peoples R China

ISBN: (纸本)9798400707032

Robust principal component analysis (PCA) is an important data dimensionality reduction algorithm. The traditional method to measure recovery error by F-norm is highly sensitive to noise. This paper proposed a modified two-dimension Robust PCA (M2DRPCA). M2DRPCA uses L-p,L-q-norm to measure the recovery error and introduces the joint regularization term of L-1,L-1-norm and F-norm to constrain the low-dimensional representation matrix of the sample, which enhances the robustness to noise and effectively avoids overfitting. An algorithm for solving M2DRPCA is presented. considering that the subproblem can be divided into some blocks, the alternate direction multiplier method (ADMM) is proposed to solve M2DRPCA. The result of numerical experiments shows that M2DRPCA has smaller recovery error than other models, which proves the effectiveness of the work.

关键词： data dimensionality reduction Robust PCA ADMM Regularization

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