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How to use expert advice
- N. Cesa-Bianchi, Y. Freund, D. Helmbold, D. Haussler, R. Schapire, Manfred K. Warmuth
- Computer ScienceSTOC
- 1 June 1993
This work analyzes algorithms that predict a binary value by combining the predictions of several prediction strategies, called `experts', and shows how this leads to certain kinds of pattern recognition/learning algorithms with performance bounds that improve on the best results currently known in this context.
On‐Line Portfolio Selection Using Multiplicative Updates
We present an on‐line investment algorithm that achieves almost the same wealth as the best constant‐rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm…
Debugging concurrent programs
A survey of current techniques used in debugging concurrent programs and systems using three general techniques are described: traditional or breakpoint style debuggers, event monitoring systems, and static analysis systems.
Learning Permutations with Exponential Weights
An algorithm for learning a permutation on-line that maintains its uncertainty about the target permutation as a doubly stochastic matrix that can be bound over the loss of the best permutation chosen in hindsight.
Predicting Nearly As Well As the Best Pruning of a Decision Tree
This paper presents a new method of making predictions on test data, and proves that the algorithm's performance will not be “much worse” than the predictions made by the best reasonably small pruning of the given decision tree, and is guaranteed to be competitive with any pruning algorithm.
How to use expert advice
- N. Cesa-Bianchi, Y. Freund, D. Haussler, D. Helmbold, R. Schapire, Manfred K. Warmuth
- Computer ScienceJACM
- 1 May 1997
This work analyzes algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts, and shows how this leads to certain kinds of pattern recognition/learning algorithms with performance bounds that improve on the best results currently know in this context.
Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks
It is shown that if the least-squares matrix Φ is symmetric and has a negative eigenvalue, then all members of a class of algorithms that perform gradient descent with identity initialization, and optionally regularize toward the identity in each layer, fail to converge.
Aerial Lidar Data Classification using AdaBoost
- S. Lodha, D. Fitzpatrick, D. Helmbold
- Environmental Science, MathematicsSixth International Conference on 3-D Digital…
- 21 August 2007
This work uses the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees, and observes that the results are robust and stable over all the various tests and algorithmic variations.
Aerial LiDAR Data Classification Using Support Vector Machines (SVM)
- S. Lodha, Edward J. Kreps, D. Helmbold, D. Fitzpatrick
- Environmental ScienceThird International Symposium on 3D Data…
- 14 June 2006
We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the support vector machine (SVM) algorithm. To do so we use five features: height, height variation,…
A dynamic disk spin-down technique for mobile computing
This work addresses the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life by using a simple and efcient algorithm based on machine learning techniques that has excellent performance in practice.