336,956 research outputs found
A K -means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis
Abstract(#br)This paper proposes a new medical diagnosis algorithm that uses a K -means interval type-2 fuzzy neural network (KIT2FNN). This KIT2FNN classifier uses a K -means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the K -means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient..
Robust Angular Synchronization via Directed Graph Neural Networks
The angular synchronization problem aims to accurately estimate (up to a
constant additive phase) a set of unknown angles from noisy measurements of their offsets
\theta_i-\theta_j \;\mbox{mod} \; 2\pi. Applications include, for example,
sensor network localization, phase retrieval, and distributed clock
synchronization. An extension of the problem to the heterogeneous setting
(dubbed -synchronization) is to estimate groups of angles
simultaneously, given noisy observations (with unknown group assignment) from
each group. Existing methods for angular synchronization usually perform poorly
in high-noise regimes, which are common in applications. In this paper, we
leverage neural networks for the angular synchronization problem, and its
heterogeneous extension, by proposing GNNSync, a theoretically-grounded
end-to-end trainable framework using directed graph neural networks. In
addition, new loss functions are devised to encode synchronization objectives.
Experimental results on extensive data sets demonstrate that GNNSync attains
competitive, and often superior, performance against a comprehensive set of
baselines for the angular synchronization problem and its extension, validating
the robustness of GNNSync even at high noise levels
A Comparative Analysis of Classification Techniques on Categorical Data in Data Mining
In recent years, huge amount of data is stored in database which is increasing at a tremendous speed. This requires need for some new techniques and tools to intelligently analyze large data sets to acquire useful information. This growing need demands for a new research field called Knowledge Discovery in Databases (KDD) or Data Mining. The main objective of the data mining process is to extract information from a large data set and transform it into some meaningful form for further use. Classification is the one of data mining techniques which is used to classify categorical data item in a set of data into one of predefined set of classes or groups, In this paper, the goal is to provide a comprehensive analysis of different classification techniques in data mining that includes decision tree, Bayesian networks, k-nearest neighbor classifier & artificial neural network.
DOI: 10.17762/ijritcc2321-8169.15081
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
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