2009
NIPS
NeurIPS 2009
Kernel Methods for Deep Learning
Abstract
We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that we call multilayer kernel machines (MKMs). We evaluate SVMs and MKMs with these kernel functions on problems designed to illustrate the advantages of deep architectures. On several problems, we obtain better results than previous, leading benchmarks from both SVMs with Gaussian kernels as well as deep belief nets.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning
🧭
Keyword Pioneer
— deep learning
🐣
Hot Topic Early Bird
— deep learning
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
📈
Trend Setter
— Neural Network Optimization
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Machine Learning > Core Methods > Kernel Methods
Deep Learning > Optimization & Theory > Neural Network Optimization
Deep Learning > Models > Neural Networks