51,460 research outputs found
The category proliferation problem in ART neural networks
This article describes the design of a new model IKMART, for classification of documents and their incorporation into categories based on the KMART architecture. The architecture consists of two networks that mutually cooperate through the interconnection of weights and the output matrix of the coded documents. The architecture retains required network features such as incremental learning without the need of descriptive and input/output fuzzy data, learning acceleration and classification of documents and a minimal number of user-defined parameters. The conducted experiments with real documents showed a more precise categorization of documents and higher classification performance in comparison to the classic KMART algorithm.Web of Science145634
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
User information needs vary significantly across different tasks, and
therefore their queries will also differ considerably in their expressiveness
and semantics. Many studies have been proposed to model such query diversity by
obtaining query types and building query-dependent ranking models. These
studies typically require either a labeled query dataset or clicks from
multiple users aggregated over the same document. These techniques, however,
are not applicable when manual query labeling is not viable, and aggregated
clicks are unavailable due to the private nature of the document collection,
e.g., in email search scenarios. In this paper, we study how to obtain query
type in an unsupervised fashion and how to incorporate this information into
query-dependent ranking models. We first develop a hierarchical clustering
algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine
query types. Then, we study three query-dependent ranking models, including two
neural models that leverage query type information as additional features, and
one novel multi-task neural model that views query type as the label for the
auxiliary query cluster prediction task. This multi-task model is trained to
simultaneously rank documents and predict query types. Our experiments on tens
of millions of real-world email search queries demonstrate that the proposed
multi-task model can significantly outperform the baseline neural ranking
models, which either do not incorporate query type information or just simply
feed query type as an additional feature.Comment: CIKM 201
Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network
Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.Comment: Preprint for Journal Machine Learnin
From Data Topology to a Modular Classifier
This article describes an approach to designing a distributed and modular
neural classifier. This approach introduces a new hierarchical clustering that
enables one to determine reliable regions in the representation space by
exploiting supervised information. A multilayer perceptron is then associated
with each of these detected clusters and charged with recognizing elements of
the associated cluster while rejecting all others. The obtained global
classifier is comprised of a set of cooperating neural networks and completed
by a K-nearest neighbor classifier charged with treating elements rejected by
all the neural networks. Experimental results for the handwritten digit
recognition problem and comparison with neural and statistical nonmodular
classifiers are given
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