258,591 research outputs found
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models
in Computer Vision and Machine Learning. The Springer Series on Challenges in
Machine Learning. Springer (2018). See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further
informatio
Testing for knowledge : maximising information obtained from fire tests by using machine learning techniques
A machine learning (ML) algorithm was applied to predict the onset of flashover in 1:5 scale Room Corner Test experiments with sandwich panels. Towards this end, a penalized logistic regression model was chosen to detect the relevant variables and consequently provided a tool that can be used to make predictions of unseen samples. The method indicates that a deeper understanding of the contributing factors leading to flashover can be achieved. Furthermore, it allows a more nuanced ranking than currently offered by the commonly used classification methods for reaction to fire tests. The proposed methodology shows a substantial value in terms of guidance for future large and intermediate scale testing. In particular, it is foreseen that the method will be extremely useful for assessing and understanding the behaviour of innovative materials and design solutions
Learning Visual Features from Snapshots for Web Search
When applying learning to rank algorithms to Web search, a large number of
features are usually designed to capture the relevance signals. Most of these
features are computed based on the extracted textual elements, link analysis,
and user logs. However, Web pages are not solely linked texts, but have
structured layout organizing a large variety of elements in different styles.
Such layout itself can convey useful visual information, indicating the
relevance of a Web page. For example, the query-independent layout (i.e., raw
page layout) can help identify the page quality, while the query-dependent
layout (i.e., page rendered with matched query words) can further tell rich
structural information (e.g., size, position and proximity) of the matching
signals. However, such visual information of layout has been seldom utilized in
Web search in the past. In this work, we propose to learn rich visual features
automatically from the layout of Web pages (i.e., Web page snapshots) for
relevance ranking. Both query-independent and query-dependent snapshots are
considered as the new inputs. We then propose a novel visual perception model
inspired by human's visual search behaviors on page viewing to extract the
visual features. This model can be learned end-to-end together with traditional
human-crafted features. We also show that such visual features can be
efficiently acquired in the online setting with an extended inverted indexing
scheme. Experiments on benchmark collections demonstrate that learning visual
features from Web page snapshots can significantly improve the performance of
relevance ranking in ad-hoc Web retrieval tasks.Comment: CIKM 201
Learning Parse and Translation Decisions From Examples With Rich Context
We present a knowledge and context-based system for parsing and translating
natural language and evaluate it on sentences from the Wall Street Journal.
Applying machine learning techniques, the system uses parse action examples
acquired under supervision to generate a deterministic shift-reduce parser in
the form of a decision structure. It relies heavily on context, as encoded in
features which describe the morphological, syntactic, semantic and other
aspects of a given parse state.Comment: 8 pages, LaTeX, 3 postscript figures, uses aclap.st
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