Ira Cohen

What is she best known for?

The fields of study she is best known for:

• Artificial intelligence
• Machine learning
• Statistics

Artificial intelligence, Machine learning, Pattern recognition, Bayesian network and Data mining are her primary areas of study. Much of her study explores Machine learning relationship to Training set. As part of the same scientific family, Ira Cohen usually focuses on Pattern recognition, concentrating on Facial recognition system and intersecting with Contextual image classification.

In her study, Missing data and Bayesian probability is strongly linked to Inference, which falls under the umbrella field of Bayesian network. Her Data mining research is multidisciplinary, incorporating elements of Byte, The Internet, Search engine indexing and One-class classification. Her Facial expression research is multidisciplinary, relying on both Cauchy distribution, Segmentation and Hidden Markov model.

Her most cited work include:

• Facial expression recognition from video sequences: temporal and static modeling (761 citations)
• Correlating instrumentation data to system states: a building block for automated diagnosis and control (483 citations)
• Capturing, indexing, clustering, and retrieving system history (305 citations)

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

Her main research concerns Artificial intelligence, Machine learning, Data mining, Bayesian network and Pattern recognition. The various areas that she examines in her Machine learning study include Training set and Generative grammar. Her research in Data mining intersects with topics in The Internet, Set and State.

Her Bayesian network research focuses on Algorithm and how it relates to Similarity. Her research in the fields of Labeled data overlaps with other disciplines such as Group. Her work in Naive Bayes classifier addresses issues such as Hidden Markov model, which are connected to fields such as Segmentation.

She most often published in these fields:

• Artificial intelligence (49.49%)
• Machine learning (34.34%)
• Data mining (27.27%)

What were the highlights of her more recent work (between 2010-2017)?

• Data mining (27.27%)
• Executable (3.03%)
• Artificial intelligence (49.49%)

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

Her scientific interests lie mostly in Data mining, Executable, Artificial intelligence, Configuration item and Root. Ira Cohen interconnects Classifier and Pattern recognition in the investigation of issues within Data mining. Her biological study spans a wide range of topics, including Source code and Association.

In the subject of general Artificial intelligence, her work in Natural language user interface is often linked to Data control language, thereby combining diverse domains of study. Her Event research incorporates elements of Similarity and Algorithm. Her work in the fields of Statistics, such as Statistical parameter, overlaps with other areas such as Forgetting factor.

Between 2010 and 2017, her most popular works were:

• Machine Learning in Computer Vision (45 citations)
• Ranking and scheduling of monitoring tasks (25 citations)
• Automated detection of a system anomaly (24 citations)

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

• Artificial intelligence
• Machine learning
• Statistics

Ira Cohen mainly investigates Data mining, Executable, Configuration item, Anomaly and Series. Ira Cohen has researched Data mining in several fields, including Event, Similarity and Algorithm. Her Series research spans across into subjects like Anomaly detection, Scale and Remote sensing.