In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier...

subsequently developed. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification. It is actually equivalent...

In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These...

Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which...

Examples of where the elastic net method has been applied are: Support vector machine Metric learning Portfolio optimization Cancer prognosis It was proven...

compatible to be used in various application. Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning...

of statistical learning and the co-inventor of the support-vector machine method and support-vector clustering algorithms. Vladimir Vapnik was born to...

depth. As of 2012, three types of non-knowledge-based systems are support-vector machines, artificial neural networks and genetic algorithms. Artificial...

perspectives on support-vector machines provide a way of interpreting support-vector machines (SVMs) in the context of other regularization-based machine-learning...

A vector database, vector store or vector search engine is a database that uses the vector space model to store vectors (fixed-length lists of numbers)...

classes. The method was invented by John Platt in the context of support vector machines, replacing an earlier method by Vapnik, but can be applied to other...

of the methods tested on it. In their original paper, they use a support-vector machine to get an error rate of 0.8%. The original MNIST dataset contains...

assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from...

between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot...

the Recursive Feature Elimination algorithm, commonly used with Support Vector Machines to repeatedly construct a model and remove features with low weights...

decision trees, k-nearest neighbors, naive Bayes, support vector machines and extreme learning machines to address multi-class classification problems....

Naive Bayes classifier Perceptron Support vector machine Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic...

Another example of an algorithm in this category is the Transductive Support Vector Machine (TSVM). A third possible motivation of transduction arises through...

data retrieval: multimodal Deep Boltzmann Machines outperform traditional models like support vector machines and latent Dirichlet allocation in classification...

Examples of supervised classifiers are Naive Bayes classifiers, support vector machines, mixtures of Gaussians, and neural networks. However, research[which...

Quadratic classifier Support vector machine – Set of methods for supervised statistical learning Least squares support vector machine Choices between different...

recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning...

probability distribution or the "signed distance to the hyperplane" in a support vector machine). Deviations from the identity function indicate a poorly-calibrated...

corresponding learning algorithm. For example, one may choose to use support-vector machines or decision trees. Complete the design. Run the learning algorithm...

speed of stochastic gradient descent. In support vector machines, it can reduce the time to find support vectors. Feature scaling is also often used in...

representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be...

transductive support vector machine, or TSVM (which, despite its name, may be used for inductive learning as well). Whereas support vector machines for supervised...

researchers continued to hope that non-linear classifiers (such as support vector machines and neural networks) might be robust to adversaries, until Battista...

concept of generative pre-training (GP) was a long-established technique in machine learning. GP is a form of self-supervised learning where a model is first...