A fast pruned-extreme learning machine for classification problem
Text Mining: The state of the art and the challenges
A text mining framework consisting of two components: Text refining that transforms unstructured text documents into an intermediate form; and knowledge distillation that deduces patterns or knowledge from the intermediate form is presented.
CRCTOL: A semantic-based domain ontology learning system
This paper presents a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents and presents two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontologies.
Benchmarking Single-Image Reflection Removal Algorithms
- Renjie Wan, Boxin Shi, Ling-yu Duan, A. Tan, A. Kot
- Computer ScienceIEEE International Conference on Computer Vision
- 1 October 2017
This paper presents the first captured Single-image Reflection Removal dataset ‘SIR2’ with 40 controlled and 100 wild scenes, ground truth of background and reflection, and performs quantitative and visual quality comparisons for four state-of-the-art single-image reflection removal algorithms using four error metrics.
Rule Extraction: From Neural Architecture to Symbolic Representation
This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which…
Depth of field guided reflection removal
- Renjie Wan, Boxin Shi, A. Tan, A. Kot
- Computer ScienceInternational Conference on Information Photonics
- 19 August 2016
A multi-scale DoF computing strategy to classify edge pixels more efficiently is introduced and, based on the results of edge classification, the background and reflection layers can be separated.
On Quantitative Evaluation of Clustering Systems
Clustering refers to the task of partitioning unlabelled data into meaningful groups (clusters). It is a useful approach in data mining processes for identifying hidden patterns and revealing…
Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback
The proposed neural model, called TD fusion architecture for learning, cognition, and navigation (TD-FALCON), enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback (reinforcement) signals.
FALCON: a fusion architecture for learning, cognition, and navigation
- A. Tan
- Computer ScienceIEEE International Joint Conference on Neural…
- 25 July 2004
The proposed cognitive model, called FALCON, enables an autonomous agent to adapt and function in a dynamic environment and is able to adapt amazingly well and learns rapidly through it's interaction with the environment in an online and incremental manner.
Cascade ARTMAP: integrating neural computation and symbolic knowledge processing
- A. Tan
- Computer ScienceIEEE Trans. Neural Networks
- 1 March 1997
This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a…