9,827 research outputs found
Combination of linear classifiers using score function -- analysis of possible combination strategies
In this work, we addressed the issue of combining linear classifiers using
their score functions. The value of the scoring function depends on the
distance from the decision boundary. Two score functions have been tested and
four different combination strategies were investigated. During the
experimental study, the proposed approach was applied to the heterogeneous
ensemble and it was compared to two reference methods -- majority voting and
model averaging respectively. The comparison was made in terms of seven
different quality criteria. The result shows that combination strategies based
on simple average, and trimmed average are the best combination strategies of
the geometrical combination
Integrating Specialized Classifiers Based on Continuous Time Markov Chain
Specialized classifiers, namely those dedicated to a subset of classes, are
often adopted in real-world recognition systems. However, integrating such
classifiers is nontrivial. Existing methods, e.g. weighted average, usually
implicitly assume that all constituents of an ensemble cover the same set of
classes. Such methods can produce misleading predictions when used to combine
specialized classifiers. This work explores a novel approach. Instead of
combining predictions from individual classifiers directly, it first decomposes
the predictions into sets of pairwise preferences, treating them as transition
channels between classes, and thereon constructs a continuous-time Markov
chain, and use the equilibrium distribution of this chain as the final
prediction. This way allows us to form a coherent picture over all specialized
predictions. On large public datasets, the proposed method obtains considerable
improvement compared to mainstream ensemble methods, especially when the
classifier coverage is highly unbalanced.Comment: Published at IJCAI-17, typo fixe
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a ‘shot’ based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was ‘broadcast’ based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
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