Search or ask a question

How to calculate slack variables SVM?

Anomaly detection

Support vector machine

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Journal Article•DOI Timing Yield Slack for Timing Yield-Constrained Optimization and Its Application to Statistical Leakage Minimization Eun Ju Hwang, Wook Kim, Young Hwan Kim - Show less +2 more 01 Oct 2013-IEEE Transactions on Very Large Scale Integration Systems 11 Citations	This paper proposes an effective way to use the TY slack for successful statistical optimization.
Open access•Journal Article•DOI Emphatic Constraints Support Vector Machine Mostafa Sabzekar, Hadi Sadoghi Yazdi, Mahmoud Naghibzadeh, Sohrab Effati - Show less +3 more 12 Apr 2010-International Journal of Computer and Electrical Engineering 4 Citations	Increasing the values of slack variables, help in reducing the effect of noisy support vectors.
Open access•Book Chapter•DOI One class classification for anomaly detection: support vector data description revisited Eric J. Pauwels, Onkar Ambekar - Show less +1 more 30 Aug 2011 32 Citations	In this paper we argue that the SVDD slack variables lack a clear geometric meaning, and we therefore re-analyze the cost function to get a better insight into the characteristics of the solution.
Open access•Book Chapter•DOI One class classification for anomaly detection: support vector data description revisited Eric J. Pauwels, Onkar Ambekar - Show less +1 more 30 Aug 2011 32 Citations	We also introduce and analyze two new definitions of slack variables and show that one of the proposed methods behaves more robustly with respect to outliers, thus providing tighter bounds compared to SVDD.
Journal Article•DOI Improved neural network for SVM learning Davide Anguita, A. Boni - Show less +1 more 01 Sep 2002-IEEE Transactions on Neural Networks 45 Citations	We show that this formulation contains some unnecessary circuits which, furthermore, can fail to provide the correct value of one of the SVM parameters and suggest how to avoid these drawbacks.

My columns

Related Questions

What is Slack as a collaborative software?5 answersSlack is a cloud-based team collaborative application that facilitates efficient real-time communication and document sharing. It is widely used in various settings, including academic medical centers, research teams, and online learning environments. Slack archives messages, hosts documents, integrates with other applications like Google Docs, and offers both desktop and mobile versions for seamless communication. It enhances team productivity by providing streamlined communication channels, improving efficiency, and supporting collaboration among team members. Users appreciate Slack for its ability to simplify communication processes, enhance learning activities, and promote collaborative development of skills. Overall, Slack serves as a versatile and effective tool for modern learners, research professionals, and educators seeking to enhance teamwork and communication in diverse environments.

What is the difference between artifical and slack variables?5 answersArtificial variables and slack variables are different concepts. Artificial variables are introduced in linear programming to help solve problems with inequality constraints by converting them into equality constraints. They are used to find an initial feasible solution for the problem. The use of artificial variables can increase the dimension of the problem and may lead to numerical instability. On the other hand, slack variables are used to convert inequalities into equalities in constrained optimization problems. They are added to the problem to represent the surplus or excess resources available. Slack variables are commonly used in various fields such as organization management, algebraic geometry, and mixture experiments. Unlike artificial variables, slack variables are not introduced to find an initial feasible solution but rather to represent the relationship between resources and constraints in the problem.

How to calculate SVM using RBF kernel manually?5 answersTo calculate SVM using RBF kernel manually, there are several approaches presented in the literature. One approach is to use stochastic logic for the computation of the RBF kernel. Another approach is to use a bilinear grid search method to determine the parameters of the SVM with RBF kernel. Additionally, a genRBF kernel has been proposed to handle incomplete data, which generalizes the classical Gaussian RBF kernel. Another alternative is the random radial basis function (RRBF) kernel, which allows for randomly assigning all kernel parameters. These different methods offer various advantages and can be implemented depending on the specific requirements of the SVM calculation.

How to calculate slack variables in SVM?11 answers

What is SVM Linux?6 answers

What package is SVM in R?5 answers

See what other people are reading

How gadgets affect the performance of the students in highschool?

Gadgets, particularly smartphones and electronic devices, have a significant impact on high school students' academic performance. Research shows that students often use gadgets during classes, with varying effects on their learning capabilities and performance. Excessive gadget use can lead to dependency, affecting both academic achievement and health status. Machine learning techniques have been employed to analyze the addictiveness of gadgets among students and their impact on academic performance, showcasing the need for monitoring and regulation of gadget usage in educational settings. Positive gadget use can enhance learning capabilities and contribute positively to academic performance, emphasizing the importance of promoting beneficial gadget utilization while curbing negative impacts for improved student outcomes.How does traffic noise modeling using CRTN differ from traditional noise modeling techniques?

Traffic noise modeling using Continuous Road Traffic Noise (CRTN) differs from traditional noise modeling techniques by offering high prediction accuracy and flexibility. CRTN utilizes road surveillance video data to analyze dynamic traffic and environmental factors, enabling precise noise mapping at the lane level with an average error of 1.53 dBA. In contrast, traditional noise modeling techniques often rely on statistical methods or regression techniques, which may be limited by the availability of calibration data and field measurements. CRTN's approach eliminates the need for field measurements, making it adaptable to various road traffic conditions without compromising prediction accuracy, as evidenced by its 93.93% accuracy in predicting noise levels. This highlights CRTN's effectiveness in accurately predicting and evaluating highway noise impact early on, surpassing the limitations of traditional noise modeling methods.Can frequency of gadget use affect the knowledge of students on AI?

The frequency of gadget use can indeed impact students' knowledge, including their understanding of Artificial Intelligence (AI). Research has shown that excessive gadget use, leading to addiction, can have negative effects on students' mental and physical health. Specifically, gadget addiction has been found to correlate negatively with students' knowledge levels of traditional games, indicating a potential impact on cognitive development. Moreover, studies have highlighted that students, including those in higher education, are at risk of becoming addicted to gadgets, which can affect their academic achievement and overall well-being. Therefore, it is crucial to manage gadget use effectively to prevent adverse consequences on students' knowledge acquisition, including in areas like AI.How well does a student detects an artificial intelligence content and distinguishes from AI content and human made content?

Students can effectively detect AI-generated content from human-written content with high accuracy using machine learning algorithms like Support Vector Machines (SVM). These algorithms, when trained on responses from Computer Science students for essay and programming assignments, have shown potential in differentiating between AI-generated and human-written text based on accuracy, computational efficiency, and confusion matrices. While AI language models like ChatGPT can produce text that is linguistically similar to human writing, subtle errors in AI-generated content can still be identified by students, especially in scientific contexts. By utilizing features like writing style, coherence, consistency, and argument logistics, students can distinguish between AI-generated and human-written scientific text, highlighting areas where AI content may lack depth and quality.How does the use of Variational Autoencoders (VAE) affect the accuracy of anomaly detection in machine learning models?

The utilization of Variational Autoencoders (VAEs) in anomaly detection significantly enhances accuracy in machine learning models. VAEs aid in learning interpretable representations of latent spaces, enabling effective anomaly detection based on reconstruction loss and Z-scores. Moreover, VAEs, when integrated with convolutional and recurrent structures, such as ConvLSTM, contribute to capturing spatial and temporal dependencies in multi-sensor data, leading to robust high-level representations and superior anomaly detection performance. Additionally, attention-based VAE architectures, like A-VAE, designed with 2D CNN and BiLSTM layers, facilitate accurate anomaly detection in traffic videos by leveraging deep learning techniques for representation learning. These findings collectively demonstrate the pivotal role of VAEs in enhancing anomaly detection accuracy across various domains.Is mart city project costly to implement in general?

Implementing Smart City projects can be costly due to various factors such as infrastructure development, technology integration, and data collection. The expense of Smart City projects is highlighted in different contexts. For instance, the research by Guna and Ariana discusses the impact of additional labor and overtime on project costs, indicating potential cost increases. Additionally, Ragab and Sabir emphasize the importance of anomaly detection in Smart City Infrastructures, which involves the use of advanced technologies like Deep Learning models, indicating the investment required for such systems. Moreover, Figliozzi and Bertini point out the challenges related to data accessibility and integration in Smart City planning, which can contribute to the overall costs of implementing Smart City initiatives. These insights collectively suggest that Smart City projects can indeed be costly to implement.What is the accuracy of SVM, ANN, and PNN in predicting diabetes?

In predicting diabetes, the accuracy of Support Vector Machine (SVM) was found to be 74% for the Pima Indian diabetic dataset, while Artificial Neural Network (ANN) achieved an accuracy of 73.6%. Additionally, the Precision Neural Network (PNN) was not specifically mentioned in the contexts provided. However, the Hybrid Random Forest algorithm outperformed other machine learning algorithms with an accuracy of 90.4% in predicting diabetes. Moreover, Logistic Regression, Naive Bayes, Support Vector Machine Classifier, and Decision Tree algorithms were used to predict type 2 diabetes with an accuracy of about 94% in a study involving the PIMA Indian Diabetic Dataset. These findings collectively highlight the effectiveness of machine learning algorithms in predicting diabetes accurately.Since when were investors' sentiments taken into account in predicting the financial markets?

Investors' sentiments have been considered in predicting financial markets in recent years, with a growing interest in sentiment analysis for stock price forecasting using machine learning techniques. Studies have explored the impact of investor sentiment derived from social media platforms like Stocktwits on predicting stock price movements, highlighting challenges such as inaccuracies in sentiment classification and the lack of user-initiated sentiment data. Furthermore, sentiment extracted from financial news and tweets has been correlated with market volatility and returns, indicating a relationship between sentiment measures and stock market movements. Incorporating sentiment analysis into regression models has shown improvements in predicting the evolution of stock opening prices, emphasizing the influence of investor sentiments on financial market dynamics.Which machine learning techniques are most effective for SOC estimation under different operating conditions?

Bagging and ExtraTree algorithms have been identified as substantially better than other machine learning methods for State of Charge (SoC) estimation. Additionally, a support vector regression algorithm (SVR) with incremental learning has shown improved estimation accuracy under dynamic operating conditions, with an average absolute error of less than 0.008. Furthermore, a hybrid model combining Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interference System (ANFIS) methods has demonstrated enhanced accuracy and speed in SoC estimation, achieving an RSME of 0.0004128, significantly outperforming individual models. These findings collectively suggest that a combination of Bagging and ExtraTree algorithms, SVR with incremental learning, and hybrid ANN-ANFIS models are the most effective machine learning techniques for accurate SoC estimation under various operating conditions.What are the advantages and disadvantages of using human translators versus machine translators for different types of translations?

Human translators offer superior use of attitude markers compared to machine translators, while the latter excel in producing native-like directives. In terms of translation accuracy, human translators maintain stability above 85% even with longer sentences, outperforming machine translators. However, machine translators, especially those from Western countries, are better at translating informative texts but struggle with culture-loaded words. On the other hand, Chinese-produced machine translators handle culture-loaded words better in vocative texts. Machine translators have the advantage of fast translation speed and lower costs, but they often produce unnatural texts, necessitating evaluation methods to prevent misuse. Ultimately, while human translators offer nuanced understanding and accuracy, machine translators provide efficiency and cost-effectiveness but may require post-editing for quality assurance.What is the correlation between the use of gadgets and academic performance among students?

The correlation between gadget use and academic performance among students is a topic of interest in recent research. Studies indicate that electronic devices like smartphones can have both positive and negative impacts on students' academic achievements. While gadgets can serve as convenient study aids, excessive use during lectures and study time may lead to decreased academic performance. Additionally, the addictive nature of electronic gadgets can affect students' mental and physical health, potentially influencing their academic success. Research on secondary school students in urban and rural areas suggests that the positive use of gadgets can enhance learning capabilities and contribute positively to academic performance, emphasizing the need for promoting responsible gadget use and implementing regulations to curb negative impacts.