搜索结果: 1-15 共查到“统计学其他学科 learning”相关记录17条 . 查询时间(0.171 秒)
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has se...
Reinforcement Learning for the Soccer Dribbling Task
Reinforcement Learning Soccer Dribbling Task
2013/6/17
We propose a reinforcement learning solution to the \emph{soccer dribbling task}, a scenario in which a soccer agent has to go from the beginning to the end of a region keeping possession of the ball,...
Online Learning in a Contract Selection Problem
Online Learning Contract Selection Problem
2013/6/14
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitab...
Common Mistakes when Applying Computational Intelligence and Machine Learning to Stock Market modelling
Computational intelligence machine learning stock market equities automated stock tradin mistakes.
2012/9/17
For a number of reasons, computational intelligence and machine learning methods have been largely dismissed by the professional community. The reasons for this are numerous and ...
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Data Representation Nonnegtive Matrix Factorization Graph Regularization Multiple Kernel Learning.
2012/9/18
Nonnegative Matrix Factorization (NMF) has been contin-uously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank ...
Learning LiNGAM based on data with more variables than observations
LiNGAM based variables observations
2012/9/17
A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory net-works with no prior knowledge of causal connectivity. Many m...
We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Speci cally if the sequence encountered by the learner is described well by a know...
Information-theoretic Dictionary Learning for Image Classification
Dictionary learning information theory mutual Dictionary learning information theory mutual
2012/9/18
We present a two-stage approach for learning dic-tionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, d...
Distance Metric Learning for Kernel Machines
metric learning distance learning support vector machines semi-de nite programming Mahalanobis distance
2012/9/17
Recent work in metric learning has signi cantly improved the state-of-the-art ink-nearest neighbor classi cation. Support vector machines (SVM), particularly with RBF kernels, are amongst the most pop...
Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods
Detecting Events Patterns Large-Scale User Generated Textual Streams Statistical Learning Methods
2012/9/18
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is pu...
One Permutation Hashing for Efficient Search and Learning
Permutation Hashing Efficient Search Learning
2012/9/18
Minwise hashing is a standard procedure in the context of search, for efficiently estimating set similari-ties in massive binary data such as text. Recently, the method ofb-bit minwise hashing has bee...
Learning Theory Approach to Minimum Error Entropy Criterion
minimum error entropy learning theory Renyi’s entropy empirical risk minimization approximation error
2012/9/18
We consider the minimum error entropy (MEE) criterion and anempirical risk minimization learning algorithm in a regression setting. Alearning theory approach is presented for this MEE algorithm and ex...
Parameter and Structure Learning in Nested Markov Models
Parameter Structure Learning in Nested Markov Models
2012/9/19
The constraints arising from DAG mod-els with latent variables can be naturally represented by means of acyclic directed mixed graphs (ADMGs). Such graphs contain directed (!) and bidirected ($) arrow...
Surrogate Losses in Passive and Active Learning
active learning sequential design selective sampling statistical learning theory surrogate loss functions classification
2012/9/19
Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled da...
Distinct counting with a self-learning bitmap
Distinct counting sampling streaming data bitmap
2011/7/19
Counting the number of distinct elements (cardinality) in a dataset is a fundamental problem in database management. In recent years, due to many of its modern applications, there has been significant...