Advances in kernel methods: support vector learning

By Bernhard Schölkopf, C. Burges, Alex Smola. 1999

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “upervised DL (Sec. 5). In the new millennium, deep NNs have ﬁnally attracted wide-spread attention, mainly by outperforming alternative machine learning methods such as kernel machines (Vapnik, 1995; Scholkopf et al., 1998)¨ in numerous important applications. In fact, since 2009, supervised deep NN”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, background): “Unlike feedforward NNs (FNNs; [95, 23]) and Support Vector Machines (SVMs; [287, 253]), RNNs can in principle interact with a dynamic partially observable environment in arbitrary, computable ways, creating and processing memories of sequences of input patterns [258].”
Survey on SVM and their application in image classification (2018, background): “This is due to the dot product in the kernel [16].”
Incremental and Decremental Support Vector Machine Learning (2000, methodology): “The incremental procedure is reversible, and decremental “unlearning” offers an efficient method to exactly evaluate leave-one-out generalization performance.”
Actionness Ranking with Lattice Conditional Ordinal Random Fields (2014, methodology): “The ranking SVM [14] proposes an svm-based ranking method.”
Fast Training of Object Detection Using Stochastic Gradient Descent (2010, unknown): “Note that for our execution of SVMLight, we also adjust the threshold to achieve optimal performance (when using the bias from the optimization process over the manually set value, the AUC drops from 44 .”
Support vector machines: hype or hallelujah? (2000, methodology): “Users interested in actually using SVMs should consult more thorough treatments such as the books by Cristianini and Shawe-Taylor [14], Vapnik's books on statistical learning theory [65][66] and recent edited volumes [ 50 ] [56].”
Reinforcement Learning Upside Down: Don't Predict Rewards - Just Map Them to Actions (2019, background): “5), certain SL methods will ignore most of the training sequences defined by the pairs (k, j) of Step 2, and instead select only a few of them, either randomly, or by selecting prototypical sequences, inspired by support vector machines (SVMs) whose only effective training examples are the support v”
Applying Discrete PCA in Data Analysis (2004, background): “0 (Joachims, 1999) classifier with default settings.”
Group-sensitive multiple kernel learning for object categorization (2009, methodology): “Inspired by SVM [21], training can be implemented by solving the following optimization problem, which maximizes the margin between positive and negative classes as well as minimizes the classification error.”