Classification, Novelty Detection and Clustering for Point Pattern Data

QR Code

Classification, Novelty Detection and Clustering for Point Pattern Data

Point pattern data, also known as multiple instance data or bags, are abundant in nature and applications. However, machine learning problems for point patterns have not received much attention. In this work, we solve three fundamental machine learning problems, namely classification, novelty detect...

Full description

Bibliographic Details
Main Author:	Tran, Quang Nhat
Format:	Thesis
Published:	Curtin University 2017
Online Access:	http://hdl.handle.net/20.500.11937/59025

Similar Items

Model-based classification and novelty detection for point pattern data
by: Vo, Ba Tuong, et al.
Published: (2017)

Clustering for point pattern data
by: Tran, N., et al.
Published: (2017)

Classification-based novelty search for patent application in UPSTO
by: Muhamad Sharhabil, Noorfatin, et al.
Published: (2011)

Toothpaste Novelty and Reality
by: Makky, Essam A., et al.
Published: (2015)

Model-based learning for point pattern data
by: Vo, Ba-Ngu, et al.
Published: (2018)

An improved system for sentence-level novelty detection in textual streams
by: Fu, Xinyu, et al.
Published: (2016)

The Congruence-Novelty Paradox: Exploring the Effects of Self-congruence and Novelty on Memorable Tourist Experiences
by: Kitin, Justin James
Published: (2023)

Enhanced Supervised Fuzzy Clustering For Pattern Classification
by: Lim , Kian Ming
Published: (2010)

A novelty detection technique for machine condition monitoring using S. O. M
by: M. L. Dennis,
Published: (2008)

Optimising a one-class SVM for geographic object based novelty detection.
by: Fourie, Christopher, et al.
Published: (2011)

A case study of the flipped classroom approach for translation studies in Vietnam
by: Nguyen, Quang Nhat
Published: (2021)

Semi-automated segment generation for geographic novelty detection using edge and area metrics
by: Fourie, C., et al.
Published: (2012)

Hybridisation on coral reefs and the conservation of evolutionary novelty
by: Richards, Zoe, et al.
Published: (2015)

Seawater for the novelty era of communications-opportunities and challenges
by: Elmutasim, Imadeldin Elsayed, et al.
Published: (2022)

Classification of Geochemical and Petrophysical Data by Using Classification of Geochemical and Petrophysical Data by Using Fuzzy Clustering
by: Kieu, D., et al.
Published: (2015)

Novitect; a novelty detection soft-computing algorithm for Structural Health Monitoring (SHM) system
by: Mustapha, Faizal, et al.

Novelty-facilitated extinction and the reinstatement of conditional human fear
by: Lucas, Katherine, et al.
Published: (2018)

Pattern Detection Approach To Boolean Function Classification
by: Mustapha, Muhazam
Published: (2004)

Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
by: Wan Maseri, Wan Mohd, et al.

Multi-class Pattern Classification in Imbalanced Data
by: Ghanem, Amal, et al.
Published: (2010)

Classification and pattern discovery of mood in weblogs
by: Nguyen, Thin, et al.
Published: (2010)

Weak-form Efficiency Market Hypothesis: An empirical study on Vietnam stock market
by: Tran, Nhat
Published: (2016)

Biomarker clustering of colorectal cancer data to complement clinical classification
by: Roadknight, Chris, et al.
Published: (2012)

Foreign and domestic collaboration, product innovation novelty, and firm growth
by: Hsieh, Wan-Lin, et al.
Published: (2018)

From Cells to Structures to Evolutionary Novelties: Creating a continuum
by: Boisvert, Catherine
Published: (2013)

Understanding the need for novelty from the perspective of self-determination theory
by: González-Cutre, D., et al.
Published: (2016)

Finding spatio-temporal patterns in climate data using clustering
by: Md. Sap, Mohd. Noor, et al.
Published: (2005)

Intrusion detection based on k-means clustering and OneR classification
by: Muda, Zaiton, et al.
Published: (2011)

Intrusion detection based on K-means clustering and Naïve Bayes classification
by: Muda, Zaiton, et al.
Published: (2011)

MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
by: Wan Maseri, Wan Mohd, et al.
Published: (2012)

Incremental clustering of dynamic data streams using connectivity based representative points
by: Luhr, Sebastian, et al.
Published: (2009)

Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
by: Raheem, Ajiboye Adeleke, et al.
Published: (2014)

Reducing greenhouse energy consumption using novelty rooftop: a simulation
by: Harjunowibowo, Dewanto, et al.
Published: (2017)

How can the word "NEW" evoke consumers' experiences of novelty and interest?
by: Sung, Billy, et al.
Published: (2016)

The Evolution, Development, and Morphology of Appendicular and Skeletal Novelties of Early Jawed Vertebrates
by: Pears, Jacob Benjamin
Published: (2023)

Identifying clinical course patterns in SMS data using cluster analysis
by: Kent, Peter, et al.
Published: (2012)

The relationship of bitumen content, aggregate surface area, and extraction time using asphalt ignition furnace
by: Trang Nhat, Tran
Published: (2008)

Forward-Backward smoothing for hidden markov models of point pattern data
by: Dam, N., et al.
Published: (2018)

Unsupervised Anomaly Detection with Unlabeled Data Using Clustering
by: Chimphlee, Witcha, et al.
Published: (2005)

Testing cluster analysis on combined petrophysical and geochemical data for rock mass classification
by: Kitzig, M., et al.
Published: (2017)

Cannot write session to /tmp/vufind_sessions/sess_l91o81ioirah4mg5h3qtrckk2k