Shu Jia Zhang

Bio: Shu Jia Zhang is an academic researcher from University of Wollongong. The author has contributed to research in topics: Null graph & Multilayer perceptron. The author has an hindex of 2, co-authored 2 publications receiving 14 citations.

Supervised encoding of graph-of-graphs for classification and regression problems

Abstract: This paper introduces a novel approach for processing a general class of structured information, viz., a graph of graphs structure, in which each node of the graph can be described by another graph, and each node in this graph, in turn, can be described by yet another graph, up to a finite depth. This graph of graphs description may be used as an underlying model to describe a number of naturally and artificially occurring systems, e.g. nested hypertexted documents. The approach taken is a data driven method in that it learns from a set of examples how to classify the nodes in a graph of graphs. To the best of our knowledge, this is the first time that a machine learning approach is enabled to deal with such structured problem domains. Experimental results on a relatively large scale real world problem indicate that the learning is efficient. This paper presents some preliminary results which show that the classification performance is already close to those provided by the state-of-the-art ones.

A fully recursive perceptron network architecture

Abstract: This paper introduces a fully recursive perceptron network (FRPN) architecture as an alternative to multilayer perceptron (MLP) with multiple hidden layers networks, popularly known as deep neural networks The FRPN consists of an input layer, an output layer, and only one hidden layer in which the hidden layer neurons are fully connected with algebraic (instantaneous) connections, and not delayed connections The FRPN is particularly attractive as an alternative to deep MLP since the FRPN eliminates the need of obtaining the number of hidden layers and the number of neurons per hidden layer Some insight into the operational mechanisms of the FRPN is obtained through an application to a practical learning problem, viz, the handwritten digit recognition problem

Protein complex analysis: From raw protein lists to protein interaction networks

Abstract: The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

Overview of the INEX 2009 XML mining track: clustering and classification of XML documents

Abstract: This report explains the objectives, datasets and evaluation criteria of both the clustering and classification tasks set in the INEX 2009 XML Mining track. The report also describes the approaches and results obtained by the different participants.

Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations.

Abstract: Research on artificial development, reinforcement learning, and intrinsic motivations like curiosity could profit from the recently developed framework of multi-objective reinforcement learning. The combination of these ideas may lead to more realistic artificial models for life-long learning and goal directed behavior in animals and humans.

Analysis techniques for illicit Bitcoin transactions

Abstract: This comprehensive overview of analysis techniques for illicit Bitcoin transactions addresses both technical, machine learning approaches as well as a non-technical, legal and governance considerations. We focus on the field of ransomware countermeasures to illustrate our points.