Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To a...
详细信息
With the growing data privacy concerns, federated machinelearning algorithms capable of preserving the confidentiality of sensitive information while enabling collaborative model training across decentralized data so...
详细信息
ISBN:
(纸本)9798400706813
With the growing data privacy concerns, federated machinelearning algorithms capable of preserving the confidentiality of sensitive information while enabling collaborative model training across decentralized data sources are attracting increasing interest. In this paper, we address the problem of collaboratively learning effective ranking models from non-independently and identically distributed (non-IID) training data owned by distinct search clients. We assume that the learning agents cannot access each other's data, and that the models learned from local datasets might be biased or underperforming due to a skewed distribution of certain document features or query topics in the learning-to-rank training data. Thus, we aim to instill in the local ranking model learned from local data the knowledge from other models to obtain a more robust ranker capable of effectively handling documents and queries underrepresented in the local collection. To achieve this, we explore different methods for merging the ranking models, thus obtaining in each client a model that excels in ranking documents from the local data distribution but also performs well on queries retrieving documents having distributions typical of a partner's node. In particular, our findings suggest that by relying on a linear combination of the local models, we can improve IR models effectiveness by up to +17.92% in NDCG@10 (moving from 0.619 to 0.730), and by up to +19.64% in MAP (moving from 0.713 to 0.853).
This Support vector machine (SVM) is one of the most popular linear classifiers in machinelearning. It thrives on pattern recognition, numeral prediction, and, of course, classification. SVM aims to maximize the marg...
详细信息
ISBN:
(纸本)9781665422093
This Support vector machine (SVM) is one of the most popular linear classifiers in machinelearning. It thrives on pattern recognition, numeral prediction, and, of course, classification. SVM aims to maximize the margin between the classes by drawing a line (hyperplane in dimensions greater than 2) that distinctly classifies the data points of each class. In this paper, we will test the performance of SVM combined with the n-gram technique on different popular databases (sentiment140, Amazon reviews, etc.) by using multiple metrics: Accuracy, Recall, Precision and F1-score.
In traditional distributed machinelearning (ML) systems, infrastructure drift refers to the deviation of underlying computational resources and network conditions over time, leading to reduced system performance and ...
详细信息
This study examines the role of Environmental, Social, and Governance (ESG) management in corporate strategy, particularly focusing on predicting ESG ratings with machinelearning. Given the diverse ESG evaluation cri...
详细信息
Separating hate speech from specific instances of offensive language is one of the biggest obstacles in the study of hate speech in online life. Lexical disclosure methods are typically not very effective because they...
详细信息
In this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the da...
详细信息
ISBN:
(纸本)9781665467544
In this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the data from this capacitor were recorded and tests were carried out using machinelearning (ML) algorithms for its remaining useful life. ML algorithms were selected from the algorithms that used for regression problems. In the study, Support Vector machine (SVM), Linear Regression (LR) and Regression Trees (RT) algorithms were used. The rated powers of the analyzed capacitor are 50kVAR and 25kVAR from the active plant. The data set was created by running the capacitor continuously for 6 months and the capacity loss was examined with using ML algorithms. The algorithm that gives the best result in the regression analyzes is the LR algorithm. With the results obtained, it is possible to analyze how long the useful life of capacitors with the same characteristics have under the same stress.
machinelearning classifiers are known algorithms used to classify network intrusion detection due to the drastic growth of data, new tools are being required to handle such a large amount of data within a short time ...
详细信息
ISBN:
(纸本)9781665483506
machinelearning classifiers are known algorithms used to classify network intrusion detection due to the drastic growth of data, new tools are being required to handle such a large amount of data within a short time frame. In this Paper, we present a Model using the Apache Mahout Framework to train machinelearning classifiers Random Forest (RF), Logistic Regression (LR), and Naive Bayes (NB) on CSE-CIC-IDS2018 dataset using Chi-Square and ANOVA f-test filter-based feature selection technique on an Apache Hadoop Framework. The performance of classifiers is measured in terms of Accuracy, Kappa, Precision, Recall, and F1-Score for a comparative analysis of the various machinelearning classifiers.
This extended abstract gives a short summary of one of the keynotes for the 9th international Workshop on Artificial Intelligence and Requirements engineering (AIRE), 2022, co-located with the 30th IEEE international ...
详细信息
ISBN:
(数字)9781665460002
ISBN:
(纸本)9781665460002
This extended abstract gives a short summary of one of the keynotes for the 9th international Workshop on Artificial Intelligence and Requirements engineering (AIRE), 2022, co-located with the 30th IEEE international Requirements engineering2022conference.
Analysing economic trends and predicting future value of economic variables are key factors for assessing a country's economic outlook. This study pursues a machinelearning approach to predict country's expor...
详细信息
暂无评论