Št. zadetkov: 75
Video in druga učna gradiva
Oznake:
computer science;machine learning;reinforcement learning;robotics
Many machine learning approaches in robotics, based on reinforcement learning, inverse optimal control or direct policy learning, critically rely on robot simulators. This paper investigates a simulatorfree direct policy learning, called Preference-based Policy Learning (PPL). PPL iterates a four-st ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;semi-supervised learning
We propose a novel privacy preserving learning algorithm that achieves semi-supervised learning in graphs. In real world networks, such as disease infection over individuals, links (contact) and labels (infection) are often highly sensitive information. Although traditional semisupervised learning m ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;pattern recognition
e last few years, several approaches have been proposed for information fusion including different variants of classifier level fusion (ensemble methods), stacking and multiple kernel learning (MKL). MKL has become a preferred choice for information fusion in object recognition. However, in the case ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;recommender systems
In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approaches to recommendations consider the recommendation ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;computational learning theory
Similarity and distance functions are essential to many learning algorithms, thus training them has attracted a lot of interest. When it comes to dealing with structured data (e.g., strings or trees), edit similarities are widely used, and there exists a few methods for learning them. However, these ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;decision support;machine learning;monte carlo methods
The paper focuses on budgeted model selection, that is the selection between a set of alternative models when the ratio between the number of model assessments and the number of alternatives, though bigger than one, is low. We propose an approach based on the notion of probability of correct selecti ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;semi-supervised learning
We study sequential prediction models in cases where only fragments of the sequences are annotated with the ground-truth. The task does not match the standard semi-supervised setting and is highly relevant in areas such as natural language processing, where completely labeled instances are expensive ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning
Hierarchical modeling and reasoning are fundamental in machine intelligence, and for this the two-parameter Poisson-Dirichlet Process (PDP) plays an important role. The most popular MCMC sampling algorithm for the hierarchical PDP and hierarchical Dirichlet Process is to conduct an incremental sampl ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;bayesian learning
Matrix factorization is a popular method for collaborative prediction, where unknown ratings are predicted by user and item factor matrices which are determined to approximate a user-item matrix as their product. Bayesian matrix factorization is preferred over other methods for collaborative filteri ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;classification;supervised learning
We propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation encompassing the whole dataset. This datum-wise representation is found by using a sparsity inducing empirical risk, wh ...
Leto:
2011
Vir:
videolectures.net