What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Alexandros Iosifidis mostly deals with Artificial intelligence, Pattern recognition, Linear discriminant analysis, Feature vector and Feedforward neural network. His study looks at the intersection of Artificial intelligence and topics like Machine learning with Financial market. His work deals with themes such as Subspace topology, Fuzzy set and Computer vision, which intersect with Pattern recognition.
His studies in Linear discriminant analysis integrate themes in fields like Class discrimination and Dimensionality reduction. While the research belongs to areas of Feature vector, Alexandros Iosifidis spends his time largely on the problem of Representation, intersecting his research to questions surrounding Similarity, Codebook, Bag-of-words model and Speech recognition. His Feedforward neural network study incorporates themes from Extreme learning machine and Training set.
His most cited work include:
- Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (108 citations)
- View-Invariant Action Recognition Based on Artificial Neural Networks (106 citations)
- On the kernel Extreme Learning Machine classifier (95 citations)
What are the main themes of his work throughout his whole career to date?
Alexandros Iosifidis focuses on Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Deep learning. Artificial intelligence is frequently linked to Computer vision in his study. His Machine learning research focuses on subjects like Financial market, which are linked to Benchmark.
His work in the fields of Artificial neural network, such as Perceptron, Biological neuron model and Time delay neural network, intersects with other areas such as Layer and Operator. His Deep learning research is multidisciplinary, relying on both Data mining and Time series. His Extreme learning machine research incorporates themes from Online machine learning, Feedforward neural network and Regularization.
He most often published in these fields:
- Artificial intelligence (89.07%)
- Pattern recognition (45.75%)
- Machine learning (38.87%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (89.07%)
- Pattern recognition (45.75%)
- Machine learning (38.87%)
In recent papers he was focusing on the following fields of study:
Alexandros Iosifidis mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Artificial neural network. Artificial intelligence is often connected to Time series in his work. His Pattern recognition study frequently involves adjacent topics like Probabilistic logic.
The study incorporates disciplines such as Classifier and Benchmark in addition to Machine learning. His research integrates issues of Edge computing and Encoding in his study of Deep learning. His research investigates the connection between Artificial neural network and topics such as Inference that intersect with issues in Iterative reconstruction.
Between 2019 and 2021, his most popular works were:
- Heterogeneous Multilayer Generalized Operational Perceptron (35 citations)
- Operational neural networks (18 citations)
- Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of investigation include Artificial intelligence, Artificial neural network, Convolutional neural network, Pattern recognition and Machine learning. He integrates Artificial intelligence and Data modeling in his research. His Convolutional neural network study combines topics in areas such as Network complexity, Biological neuron model and Training set.
His study in the field of Feature vector is also linked to topics like Dimension. His Feature vector research also works with subjects such as
- Regularization together with Support vector machine,
- Discriminative model most often made with reference to Encoding. His work on Overfitting and Compressed sensing as part of general Machine learning study is frequently connected to Field, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
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