Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
[HTML][HTML] Explainable AI for operational research: A defining framework, methods, applications, and a research agenda
The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …
aiding decision-making, has become a critical requirement for many applications. For …
Unsupervised K-means clustering algorithm
The k-means algorithm is generally the most known and used clustering method. There are
various extensions of k-means to be proposed in the literature. Although it is an …
various extensions of k-means to be proposed in the literature. Although it is an …
Testing machine learning based systems: a systematic mapping
Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …
one or more components that learn how to perform a task from a given data set. The …
Machine learning and artificial intelligence
AV Joshi - 2020 - Springer
This is your last chance. After this, there is no turning back. You take the blue pill-the story
ends, you wake up in your bed and believe whatever you want to believe. You take the red …
ends, you wake up in your bed and believe whatever you want to believe. You take the red …
[图书][B] Model-based clustering and classification for data science: with applications in R
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …
leave open such central questions as: how many clusters are there? Which method should I …
Incorporating copying mechanism in sequence-to-sequence learning
We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to
as copying, in which certain segments in the input sequence are selectively replicated in the …
as copying, in which certain segments in the input sequence are selectively replicated in the …
A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
AE Ezugwu, AM Ikotun, OO Oyelade… - … Applications of Artificial …, 2022 - Elsevier
Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …
active research in many fields of study, such as computer science, data science, statistics …
The blessings of multiple causes
Causal inference from observational data is a vital problem, but it comes with strong
assumptions. Most methods assume that we observe all confounders, variables that affect …
assumptions. Most methods assume that we observe all confounders, variables that affect …
A review of novelty detection
Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …
that are available during training. This may be seen as “one-class classification”, in which a …