Classification of continuous multi-way data via dissimilarity representation
نویسنده
چکیده
Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft; op gezag van de Rector Magnificus prof. 4 Missing values in dissimilarity-based classification of multi-way data 93 4. Summary 124 Samenvatting 125 Acknowledgments 126
منابع مشابه
The Dissimilarity Representation as a Tool for Three-Way Data Classification: A 2D Measure
The dissimilarity representation has demonstrated advantages in the solution of classification problems. Meanwhile, the representation of objects by multi-dimensional arrays is necessary in many research areas. However, the development of proper classification tools that take the multi-way structure into account is incipient. This paper introduces the use of the dissimilarity representation as ...
متن کاملA New Algorithm for Optimization of Fuzzy Decision Tree in Data Mining
Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...
متن کاملClassification of three-way data by the dissimilarity representation
Representation of objects by multi-dimensional data arrays has become very common for many research areas e.g. image analysis, signal processing and chemometrics. In most cases, it is the straightforward representation obtained from sophisticated measurement equipments e.g. radar signal processing. Although the use of this complex data structure could be advantageous for a better discrimination...
متن کاملMissing Values in Dissimilarity-Based Classification of Multi-way Data
Missing values can occur frequently in many real world situations. Such is the case of multi-way data applications, where objects are usually represented by arrays of 2 or more dimensions e.g. biomedical signals that can be represented as time-frequency matrices. This lack of attributes tends to influence the analysis of the data. In classification tasks for example, the performance of classifi...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013