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What are the disadvantages of an SVM hard margin classifier? 

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We propose an extension of the standard SVM optimization in which we also account for the radius in order to produce an even tighter error bound than what we get by controlling only for the margin.
Open accessJournal ArticleDOI
14 Jan 2021
33 Citations
As a further outcome, the analysis allows for the identification of the maximum number of training samples that the hard-margin SVM is able to separate.
Open accessProceedings ArticleDOI
10 Jun 2012
17 Citations
Our formulation generalizes the traditional large margin principle used in standard SVM, that is, we maximize the margin-radius-ratio.
Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.
The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier.
Open accessBook ChapterDOI
Antoine Bordes, Léon Bottou 
03 Oct 2005
88 Citations
We propose a novel online kernel classifier algorithm that converges to the Hard Margin SVM solution.
Recent results in theoretical machine learning seem to suggest that nice properties of the margin distribution over a training set turns out in a good performance of a classifier.
Open accessJournal ArticleDOI
14 Jan 2021
33 Citations
The precise nature of our results allows for an accurate performance comparison of the hard-margin and soft-margin SVM as well as a better understanding of the involved parameters (such as the number of measurements and the margin parameter) on the classification performance.

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Why svms are not ideal for multiclass problems?4 answersSupport Vector Machines (SVMs) are not ideal for multiclass problems because they were originally designed for binary classification tasks. While SVMs can be extended to handle multiclass problems using techniques like one-versus-one (OvO) or one-versus-all (OvA), these approaches can lead to regions of inconclusive predictions. Additionally, SVMs have parameters that need to be carefully tuned for optimal performance, which can be challenging in multiclass scenarios. Furthermore, the time complexity of SVMs makes them impractical for training on large datasets with millions of data points. To address these limitations, alternative approaches such as multilevel SVMs have been developed, which build a hierarchy of problems and train SVM models for each level, resulting in faster training on huge datasets.
What are the main advantages and disadvantages of Support Vector Machines (SVM)?3 answersSupport Vector Machines (SVM) have several advantages and disadvantages. One of the main advantages is their ability to produce high-quality results with interpretability and flexibility. SVMs have been successfully used in various fields, including drug discovery and agriculture, to achieve accurate predictions. However, SVMs face challenges when dealing with big data, particularly in the case of nonlinear SVMs. The complexity of nonlinear SVM solvers and the increase in memory requirements with the number of training samples negatively affect runtime and memory usage. Additionally, parameter fitting in SVMs can further exacerbate runtime issues. Despite these challenges, SVMs have proven to be effective in many applications, and ongoing research aims to address these limitations and improve their performance.
What are the drawbacks of SVM with respect to parameter selection?5 answersSupport Vector Machines (SVM) have some drawbacks when it comes to parameter selection. One issue is that the search process for selecting the parameters can be complicated and time-consuming. Another challenge is that SVM has multiple parameters, such as the regularization parameter and the error sensitivity parameter, which need to be properly selected. Additionally, the performance of SVM can be adversely affected by poorly selected parameter values. Existing parameter tuning methods may not adequately address the complexity of choosing between different SVM models, such as those that solve an optimization problem with inequality constraints or those that solve the least square of errors. These challenges highlight the need for effective parameter selection techniques to ensure optimal performance of SVM models.
What are the advantages and disadvantages of using SVM for long text classification?5 answersSupport Vector Machines (SVM) have several advantages and disadvantages for long text classification. One advantage is that SVM is effective in handling large and high-dimensional classification problems, making it suitable for long texts. Another advantage is that SVM can categorize any type of text into a given category after being trained with labeled data, making it versatile. However, SVM also has some disadvantages. One disadvantage is that SVM requires manually labeled data samples for training, which can be time-consuming and error-prone. Another disadvantage is that SVM may not perform well when dealing with mislabeled samples, and the choice of cost sensitivity plays a crucial role in model building. Overall, SVM's effectiveness in handling large-scale data and its versatility in text classification are its main advantages, while the need for labeled data and the sensitivity to mislabeled samples are its main disadvantages.
What are the weaknesses of SVM for sentiment analysis?3 answersSupport Vector Machine (SVM) has some weaknesses in sentiment analysis. One weakness is the parameter selection, which can affect the accuracy of the model. Another weakness is that SVM sacrifices the distribution of data, which can lead to lower accuracy and stability when dealing with big and complex sentiment data. Additionally, SVM can be slower in processing speed compared to other methods like Long Short-term Memory (LSTM) in deep learning.
How can you increase the accuracy of a SVM classifier?6 answers

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