Artificial intelligence, Pattern recognition, Machine learning, Decision tree and Random subspace method are his primary areas of study. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Descriptive complexity theory and Average-case complexity. His work deals with themes such as Computational complexity theory, Hyperrectangle and Information extraction, which intersect with Pattern recognition.
Tin Kam Ho has researched Machine learning in several fields, including Decision theory, Training set and Knowledge representation and reasoning. His biological study spans a wide range of topics, including Random forest and Optical character recognition. His Overfitting research includes elements of Test data, Classifier, Ensembles of classifiers and Binary tree.
Tin Kam Ho spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Classifier and Data mining. The study incorporates disciplines such as Data complexity, Computer vision and Natural language processing in addition to Artificial intelligence. A large part of his Pattern recognition studies is devoted to Random subspace method.
His research in the fields of Overfitting, Ensemble learning and Support vector machine overlaps with other disciplines such as Competence. His Classifier study integrates concerns from other disciplines, such as Training set and Classifier. His Data mining study incorporates themes from Feature and Feature vector.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Natural language processing, Data mining and Computer vision. Tin Kam Ho integrates many fields in his works, including Artificial intelligence and Programming paradigm. His Machine learning research incorporates themes from Classifier, Data point and Pattern recognition.
His Data mining study combines topics in areas such as Domain, Mechanism, Search engine, Feature vector and Result set. As part of one scientific family, Tin Kam Ho deals mainly with the area of Computer vision, narrowing it down to issues related to the Simultaneous localization and mapping, and often Acoustics and Cluster analysis. His Anomaly detection study is concerned with Pattern recognition in general.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Data mining, Electric power and Demand forecasting. His Artificial intelligence study frequently links to other fields, such as Code. The Machine learning study combines topics in areas such as Statistical inference and Pattern recognition.
His Data mining study combines topics from a wide range of disciplines, such as Natural language processing, Concept vector, Divergence, Ground truth and Computer network. Other disciplines of study, such as Smart meter, Real-time computing, Simulation, Benchmark and Automatic meter reading, are mixed together with his Demand forecasting studies. His research integrates issues of Fingerprint, Fingerprint recognition, Graphical model, Probabilistic logic and RSS in his study of Smoothing.
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The random subspace method for constructing decision forests
Tin Kam Ho.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)
Random decision forests
Tin Kam Ho.
international conference on document analysis and recognition (1995)
Decision combination in multiple classifier systems
Tin Kam Ho;J.J. Hull;S.N. Srihari.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1994)
Complexity measures of supervised classification problems
Tin Kam Ho;M. Basu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Nearest Neighbors in Random Subspaces
Tin Kam Ho.
Lecture Notes in Computer Science (1998)
A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors
Tin Kam Ho.
Pattern Analysis and Applications (2002)
MULTIPLE CLASSIFIER COMBINATION: LESSONS AND NEXT STEPS
Tin Kam Ho.
Data Complexity in Pattern Recognition
Mitra Basu;Tin Kam Ho.
SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals
Piotr Mirowski;Tin Kam Ho;Saehoon Yi;Michael MacDonald.
international conference on indoor positioning and indoor navigation (2013)
Methods and apparatus for location determination based on dispersed radio frequency tags
Michael Andrews;Tin Ho;Gregory Kochanaki;Louis Lanzerotti.
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