Hierarchical Classification Using Binary Data
نویسندگان
چکیده
منابع مشابه
Simple Classification using Binary Data
Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this work, we study the problem of data classification from binary data and propose a framework with low computation and resource costs. We illustrate the utility of the proposed approach through stylized and realistic numerical experiment...
متن کاملHierarchical models for repeated binary data using the IBF sampler
Hierarchical models have emerged as a promising tool for the analysis of repeated binary data. However, the computational complexity in these models have limited their applications in practice. Several approaches have been proposed in the literature to overcome the computational difficulties including maximum likelihood estimation from a frequentist perspective (e.g., J.Amer. Statist.Assoc. 89 ...
متن کاملMultilevel Data Classification and Function Approximation Using Hierarchical Neural Networks
Combining diverse features and multiple classifiers is an open research area in which no optimal strategy is found but successful experimental studies have been performed depending on a specific task at hand. In this chapter, a strategy for combining diverse features and multiple classifiers is presented as an exemplary new model in multilevel data classification using hierarchical neural netwo...
متن کاملLidar Data Classification Using Hierarchical K-means Clustering
This paper deals with lidar point cloud filtering and classification for modelling the Terrain and more generally for scene segmentation. In this study, we propose to use the well-known K-means clustering algorithm that filters and segments (point cloud) data. The Kmeans clustering is well adapted to lidar data processing, since different feature attributes can be used depending on the desired ...
متن کاملExtension of mixture-of-experts networks for binary classification of hierarchical data
OBJECTIVE For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another exten...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: AI Magazine
سال: 2019
ISSN: 2371-9621,0738-4602
DOI: 10.1609/aimag.v40i2.2846