D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 19,585 125 World Ranking 8270 National Ranking 3831

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

His most cited work include:

  • The random subspace method for constructing decision forests (4173 citations)
  • Random decision forests (2215 citations)
  • Decision combination in multiple classifier systems (1409 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (60.74%)
  • Pattern recognition (33.33%)
  • Machine learning (25.19%)

What were the highlights of his more recent work (between 2013-2020)?

  • Artificial intelligence (60.74%)
  • Machine learning (25.19%)
  • Natural language processing (6.67%)

In recent papers he was focusing on the following fields of study:

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.

Between 2013 and 2020, his most popular works were:

  • A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids (86 citations)
  • Demand forecasting in smart grids (73 citations)
  • Design of the 2015 ChaLearn AutoML challenge (64 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

The random subspace method for constructing decision forests

Tin Kam Ho.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)

6968 Citations

Random decision forests

Tin Kam Ho.
international conference on document analysis and recognition (1995)

5312 Citations

Decision combination in multiple classifier systems

Tin Kam Ho;J.J. Hull;S.N. Srihari.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1994)

2276 Citations

Complexity measures of supervised classification problems

Tin Kam Ho;M. Basu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)

737 Citations

Nearest Neighbors in Random Subspaces

Tin Kam Ho.
Lecture Notes in Computer Science (1998)

257 Citations

A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors

Tin Kam Ho.
Pattern Analysis and Applications (2002)

209 Citations

MULTIPLE CLASSIFIER COMBINATION: LESSONS AND NEXT STEPS

Tin Kam Ho.
(2002)

207 Citations

Data Complexity in Pattern Recognition

Mitra Basu;Tin Kam Ho.
(2020)

205 Citations

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)

186 Citations

Methods and apparatus for location determination based on dispersed radio frequency tags

Michael Andrews;Tin Ho;Gregory Kochanaki;Louis Lanzerotti.
(2002)

183 Citations

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