[PDF][PDF] An Extension on" Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons.
In a recently published paper in JMLR, Demšar (2006) recommends a set of non-parametric
statistical tests and procedures which can be safely used for comparing the performance of …
statistical tests and procedures which can be safely used for comparing the performance of …
The clustree: indexing micro-clusters for anytime stream mining
Clustering streaming data requires algorithms that are capable of updating clustering results
for the incoming data. As data is constantly arriving, time for processing is limited. Clustering …
for the incoming data. As data is constantly arriving, time for processing is limited. Clustering …
Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm
A concerted research effort over the past two decades has heralded significant
improvements in both the efficiency and effectiveness of time series classification. The …
improvements in both the efficiency and effectiveness of time series classification. The …
Indexing and classifying gigabytes of time series under time warping
Time series classification maps time series to labels. The nearest neighbour algorithm (NN)
using the Dynamic Time Warping (DTW) similarity measure is a leading algorithm for this …
using the Dynamic Time Warping (DTW) similarity measure is a leading algorithm for this …
Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification
Abstract Averaged n-Dependence Estimators (A n DE) is an approach to probabilistic
classification learning that learns by extrapolation from marginal to full-multivariate …
classification learning that learns by extrapolation from marginal to full-multivariate …
Self-adaptive anytime stream clustering
Clustering streaming data requires algorithms which are capable of updating clustering
results for the incoming data. As data is constantly arriving, time for processing is limited …
results for the incoming data. As data is constantly arriving, time for processing is limited …
A naive bayes baseline for early gesture recognition
Early gesture/action recognition is the task of determining the identity of a gesture/action with
as few information as possible. Although the topic is relatively new, there are some methods …
as few information as possible. Although the topic is relatively new, there are some methods …
A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets
Given the ubiquity of time series data, the data mining community has spent significant time
investigating the best time series similarity measure to use for various tasks and domains …
investigating the best time series similarity measure to use for various tasks and domains …
Indexing density models for incremental learning and anytime classification on data streams
Classification of streaming data faces three basic challenges: it has to deal with huge
amounts of data, the varying time between two stream data items must be used best …
amounts of data, the varying time between two stream data items must be used best …
A lazy bagging approach to classification
In this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on
the characteristics of test instances. Upon receiving a test instance xk, LB trims bootstrap …
the characteristics of test instances. Upon receiving a test instance xk, LB trims bootstrap …