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Article
Open AccessAutonomous optical navigation using nanosatellite-class instruments: a Mars approach case study
This paper examines the effectiveness of small star trackers for orbital estimation. Autonomous optical navigation has been used for some time to provide local estimates of orbital parameters during close appr...
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Chapter and Conference Paper
Incorporating Positional Information into Deep Belief Networks for Sentiment Classification
Deep belief networks (DBNs) have proved powerful in many domains including natural language processing (NLP). Sentiment classification has received much attention in both engineering and academic fields. In ad...
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Article
Bagging based ensemble transfer learning
Nowadays, transfer learning is one of the main research areas in machine learning that is helpful for labeling the data with low cost. In this paper, we propose a novel bagging-based ensemble transfer learning...
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Chapter and Conference Paper
Gaussian Neuron in Deep Belief Network for Sentiment Prediction
Deep learning has been widely applied in natural language processing. The neuron model in a deep belief network is important for its performance, and so more attention should be paid to investigate how much in...
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Article
Bayesian Citation-KNN with distance weighting
Multi-instance (MI) learning is receiving growing attention in the machine learning research field, in which learning examples are represented by a bag of instances instead of a single instance. K-nearest-neig...
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Chapter and Conference Paper
Cost-Sensitive Self-Training
In some real-world applications, it is time-consuming or expensive to collect much labeled data, while unlabeled data is easier to obtain. Many semi-supervised learning methods have been proposed to deal with ...
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Article
Erratum to: Two morphologically and immunophenotypically distinct cell populations within a composite lymphoma arise from a common precursor
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Article
Two morphologically and immunophenotypically distinct cell populations within a composite lymphoma arise from a common precursor
The term composite lymphoma defines a lymphoma consisting of two or more morphologically and immunophenotypically distinct lymphomas within the same lymph node or other organ. Recently, several molecular studi...
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Chapter and Conference Paper
Instance Selection in Semi-supervised Learning
Semi-supervised learning methods utilize abundant unlabeled data to help to learn a better classifier when the number of labeled instances is very small. A common method is to select and label unlabeled instan...
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Chapter and Conference Paper
The Unsymmetrical-Style Co-training
Semi-supervised learning has attracted much attention over the past decade because it provides the advantage of combining unlabeled data with labeled data to improve the learning capability of models. Co-train...
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Chapter and Conference Paper
Semi-supervised Probability Propagation on Instance-Attribute Graphs
Graph-based methods have become one of the most active research areas of semi-supervised learning (SSL). Typical SSL graphs use instances as nodes and assign weights that reflect the similarity of instances. I...
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Chapter and Conference Paper
Semi-supervised Self-training for Sentence Subjectivity Classification
Recent natural language processing (NLP) research shows that identifying and extracting subjective information from texts can benefit many NLP applications. In this paper, we address a semi-supervised learning...
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Chapter and Conference Paper
Proper Model Selection with Significance Test
Model selection is an important and ubiquitous task in machine learning. To select models with the best future classification performance measured by a goal metric, an evaluation metric is often used to select th...
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Chapter
Understanding Driving Activity Using Ensemble Methods
Motivation for the use of statistical machine learning techniques in the automotive domain arises from our development of context aware intelligent driver assistance systems, specifically, Driver Workload Mana...
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Chapter and Conference Paper
Switching among Non-Weighting, Clause Weighting, and Variable Weighting in Local Search for SAT
One way to design a local search algorithm that is effective on many types of instances is allowing this algorithm to switch among heuristics. In this paper, we refer to the way in which non-weighting algorithm a...
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Chapter
Alertometer: Detecting and Mitigating Driver Drowsiness and Fatigue Using an Integrated Human Factors and Computer Vision Approach
A significant number of highway crashes are attributable to driver drowsiness and fatigue. Drowsiness-related crashes can often cause more serious occupant injuries than crashes that are not related to driver ...
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Chapter and Conference Paper
Probability Based Metrics for Locally Weighted Naive Bayes
Locally weighted naive Bayes (LWNB) is a successful instance-based classifier, which first finds the neighbors of the test instance using Euclidean metric, and then builds a naive Bayes model in the local neig...
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Chapter and Conference Paper
Combining Adaptive Noise and Look-Ahead in Local Search for SAT
The adaptive noise mechanism was introduced in Novelty+ to automatically adapt noise settings during the search [4]. The local search algorithm G 2 WSAT deterministically exploits prom...
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Chapter and Conference Paper
Learning Locally Weighted C4.4 for Class Probability Estimation
In many real-world data mining applications, accurate class probability estimations are often required to make optimal decisions. For example, in direct marketing, we often need to deploy different promotion s...
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Chapter and Conference Paper
Learning Naive Bayes for Probability Estimation by Feature Selection
Naive Bayes is a well-known effective and efficient classification algorithm. But its probability estimation is poor. In many applications, however, accurate probability estimation is often required in order t...
