18 Result(s)
within
Qinghua Hu
Engineering
-
Article
ULAF-Net: Ultra lightweight attention fusion network for real-time semantic segmentation
Real-time semantic segmentation, laying the foundation of mobile robots and autonomous driving, has attracted much attention in recent years. Currently, most deep models suffer high computational costs due to ...
-
Article
Exploring and exploiting hierarchical structures for large-scale classification
Classification and recognition tasks confronted by intelligent systems are becoming complicated as the sizes of samples, dimensionality and labels dramatically increase in the past few years. Learning machines...
-
Article
Unsupervised deep hashing with multiple similarity preservation for cross-modal image-text retrieval
Deep hashing cross-modal image-text retrieval has the advantage of low storage cost and high retrieval efficiency by map** different modal data into a Hamming space. However, the existing unsupervised deep h...
-
Article
Building hierarchical class structures for extreme multi-class learning
Class hierarchical structures play a significant role in large and complex tasks of machine learning. Existing studies on the construction of such structures follow a two-stage strategy. The category similarit...
-
Article
A Model-based Design of the Water Membrane Evaporator for the Advanced Spacesuit
A spacesuit water membrane evaporator (SWME) based on the hollow fiber membrane bundle is regarded as a promising technology for the advanced thermal management system of the next-generation spacesuit. This pa...
-
Article
Data reduction based on NN-kNN measure for NN classification and regression
Data reduction processes are designed not only to reduce the amount of data, but also to reduce noise interference. In this study, we focus on researching sample reduction algorithms for the classification and...
-
Article
Self-paced hierarchical metric learning (SPHML)
Metric learning aims to learn a distance to measure the difference between two samples, and it plays an important role in pattern recognition tasks. Most of the existing metric learning methods rely on pairs o...
-
Chapter and Conference Paper
Interference Emitter Localization Based on Hyperbolic Passive Location in Spectrum Monitoring
Interference signals can always be found during spectrum monitoring, which has a serious impact in the regular use of radio business [1]. Sometimes is difficult to shied it by suppress signal, so it is becoming i...
-
Article
Multi-kernel SVM based depression recognition using social media data
Depression has become the world’s fourth major disease. Compared with the high incidence, however, the rate of depression medical treatment is very low because of the difficulty of diagnosis of mental problems...
-
Article
Feature selection based on maximal neighborhood discernibility
Neighborhood rough set has been proven to be an effective tool for feature selection. In this model, the positive region of decision is used to evaluate the classification ability of a subset of candidate feat...
-
Article
Feature and instance reduction for PNN classifiers based on fuzzy rough sets
Instance reduction for K-nearest-neighbor classification rules (KNN) has attracted much attention these years, and most of the existing approaches lose the semantics of probability of original data. In this w...
-
Article
Open AccessFeature selection for monotonic classification via maximizing monotonic dependency
Monotonic classification is a special task in machine learning and pattern recognition. As to monotonic classification, it is assumed that both features and decision are ordinal and there is the monotonicity c...
-
Article
Comparative analysis on margin based feature selection algorithms
Feature evaluation and selection is an important preprocessing step in classification and regression learning. As large quantity of irrelevant information is gathered, selecting the most informative features m...
-
Article
On rough approximations of groups
It is one of useful methods for research of group theory to construct a new group by using known groups. Lower and upper approximation operators of rough sets are applied into group theory and so the notion of...
-
Chapter
Exploring Neighborhood Structures with Neighborhood Rough Sets in Classification Learning
We introduce neighborhoods of samples to granulate the universe and use the neighborhood granules to approximate classification, thus they derived a model of neighborhood rough sets. Some machine learning algo...
-
Article
Open AccessFeature Selection in Decision Systems Based on Conditional Knowledge Granularity
Feature selection is an important technique for dimension reduction in machine learning and pattern recognition communities. Feature evaluation functions play essential roles in constructing feature selection ...
-
Article
Open AccessFuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection
Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is dif...
-
Article
An efficient gene selection technique for cancer recognition based on neighborhood mutual information
Gene selection is a key problem in gene expression based cancer recognition and related tasks. A measure, called neighborhood mutual information (NMI), is introduced to evaluate the relevance between genes and...
