'Inductive bias' presentation slideshows

Getting a Machine to Fly Learn Extending Our Reach Beyond Our Grasp

Getting a Machine to Fly Learn Extending Our Reach Beyond Our Grasp. Daniel L. Silver Acadia University, Wolfville, NS, Canada danny.silver@acadiau.ca. Key Take Away. A major challenge in artificial intelligence has been how to develop common background knowledge

592 views • 47 slides

Friday, 25 January 2008 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 02 of 42. Representation Bias vs. Search Bias and Intro to Decision Trees. Friday, 25 January 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Readings: Chapter 2, Mitchell

432 views • 32 slides

2. Concept Learning

1.23k views • 31 slides

Concept Learning and the General-to-Specific Ordering

474 views • 31 slides

Inductive Transfer With Context-sensitive Neural Networks

Inductive Transfer With Context-sensitive Neural Networks. Danny Silver, Ryan Poirier, & Duane Currie Acadia University, Wolfville, NS, Canada danny.silver@acadiau.ca. Outline. Machine Lifelong Learning (ML3) and Inductive Transfer Multiple Task Learning (MTL)

416 views • 28 slides

Inductive Bias: How to generalize on novel data

Inductive Bias: How to generalize on novel data. Overfitting. Noise vs. Exceptions. Hypothesis Space. The Hypothesis space H is the set all the possible models h which can be learned by the current learning algorithm e.g. Set of possible weight settings for a perceptron

813 views • 27 slides

Learning Agents Laboratory Computer Science Department George Mason University

CS 782 Machine Learning. 9 Instance-Based Learning. Prof. Gheorghe Tecuci. Learning Agents Laboratory Computer Science Department George Mason University. Overview. Exemplar-based representation of concepts. The k-nearest neighbor algorithm. Discussion.

376 views • 24 slides

Boosting to Correct Inductive Bias in Text Classification

433 views • 17 slides

Tuesday, October 12, 1999 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 14. Midterm Review. Tuesday, October 12, 1999 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Readings: Chapters 1-7, Mitchell Chapters 14-15, 18, Russell and Norvig. Lecture 0: A Brief Overview of Machine Learning.

170 views • 16 slides

Publication Venues

Publication Venues. Main Neural Network Conferences NIPS (Neural Information Processing Systems) IJCNN (Intl Joint Conf on Neural Networks) Main Neural Network Journals Neural Networks Neural Computation IEEE Transactions on Neural Networks. Publication Venues.

272 views • 7 slides

Remarks on Inductive Bias

Remarks on Inductive Bias. Shiliang Sun ( sunsl02@mails.tsinghua.edu.cn ) PhD Student Pattern Recognition and Intelligent Systems Department of Automation Tsinghua University. Outline. What is inductive bias? Should inductive bias be avoided?

317 views • 7 slides