Titlebook: Advances in Knowledge Discovery and Data Mining; 20th Pacific-Asia Co James Bailey,Latifur Khan,Ruili Wang Conference proceedings 2016 Spri

GEAR

https://doi.org/10.1007/978-3-319-43773-6ansfer knowledge from a completed source optimisation task to a new target task in order to overcome the cold start problem. We model source data as noisy observations of the target function. The level of noise is computed from the data in a Bayesian setting. This enables flexible knowledge transfer

Isometric

Jignesh Patel MD, PhD,Jon Kobashigawa MD, degrade the performance of traditional online learning algorithms. Thus, many existing works focus on detecting concept drift based on statistical evidence. Other works use sliding window or similar mechanisms to select the data that closely reflect current concept. Nevertheless, few works study h

COUCH

W. Kim Halford,Jemima Petch,Debra Creedytering methods by letting a user select samples based on his/her knowledge. However, due to knowledge limitation, a single user may only pick out the samples that s/he is familiar with while ignore the others, such that the selected samples are often biased. We propose a framework to address this is

CREST

W. Kim Halford,Jemima Petch,Debra Creedyrd classification and regression problems where a domain expert can provide the labels for the data in a reasonably short period of time, training data in such longitudinal studies must be obtained only by waiting for the occurrence of sufficient number of events. The main objective of this work is

palliative-care

貪婪的人

Patricia R. Recupero,Samara E. Harmsence between pair-wise consecutive frames at a specific time, we measure the divergence between two OCSVM classifiers, which are learnt from two contextual sets, i.e., immediate past set and immediate future set. To speed up the processing procedure, the two OCSVM classifiers are updated in an onlin

Emmenagogue

magnanimity

Andrew Hecht,Jonathan S. Markowitzupervised nonlinear dimensionality reduction method that aims at lower space complexity is proposed. First, a positive and negative competitive learning strategy is introduced to the single layered Self-Organizing Incremental Neural Network (SOINN) to process partially labeled datasets. Then, we for

水槽

Ryan Budwany,Tony K. George,Timothy R. Deer usually have different physical interpretations. It may be inappropriate to map multiple views of data onto a shared feature space and directly compare them. In this paper, we propose a simple yet effective Cross-View Feature Hashing (CVFH) algorithm via a “partition and match” approach. The featur

Reclaim

Advances in Knowledge Discovery and Data Mining978-3-319-31753-3Series ISSN 0302-9743 Series E-ISSN 1611-3349