221,292 research outputs found
Elaboration of the New Paradigm of Interdisciplinary Investigations
In the article, the problem of construction a meta-theory for approaching the complex phenomena of Reality is discussed. The integrated information system is formulated. Such postulate is a suggested basis for creation of a unified methodology of cognition (investigation) which makes it possible to elaborate a new paradigm of interdisciplinary investigations as a separate scientific discipline which has its own methods and special objects. The article will be of interest to philosophers and methodologists of scienc
Tree-Independent Dual-Tree Algorithms
Dual-tree algorithms are a widely used class of branch-and-bound algorithms.
Unfortunately, developing dual-tree algorithms for use with different trees and
problems is often complex and burdensome. We introduce a four-part logical
split: the tree, the traversal, the point-to-point base case, and the pruning
rule. We provide a meta-algorithm which allows development of dual-tree
algorithms in a tree-independent manner and easy extension to entirely new
types of trees. Representations are provided for five common algorithms; for
k-nearest neighbor search, this leads to a novel, tighter pruning bound. The
meta-algorithm also allows straightforward extensions to massively parallel
settings.Comment: accepted in ICML 201
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
We study the problem of learning-to-learn: inferring a learning algorithm
that works well on tasks sampled from an unknown distribution. As class of
algorithms we consider Stochastic Gradient Descent on the true risk regularized
by the square euclidean distance to a bias vector. We present an average excess
risk bound for such a learning algorithm. This result quantifies the potential
benefit of using a bias vector with respect to the unbiased case. We then
address the problem of estimating the bias from a sequence of tasks. We propose
a meta-algorithm which incrementally updates the bias, as new tasks are
observed. The low space and time complexity of this approach makes it appealing
in practice. We provide guarantees on the learning ability of the
meta-algorithm. A key feature of our results is that, when the number of tasks
grows and their variance is relatively small, our learning-to-learn approach
has a significant advantage over learning each task in isolation by Stochastic
Gradient Descent without a bias term. We report on numerical experiments which
demonstrate the effectiveness of our approach.Comment: 37 pages, 8 figure
- …