Introduction to Machine Learning
https://doi.org/10.1007/978-3-030-10546-4_1
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https://doi.org/10.1007/978-3-030-10546-4_1
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments ...
https://doi.org/10.7551/mitpress/8996.001.0001
https://doi.org/10.1007/978-0-387-30164-8
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict ...
https://doi.org/10.1142/9789811201967_0001
A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product ...
https://doi.org/10.7551/mitpress/13811.001.0001
https://doi.org/10.1007/978-981-15-1967-3
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing...
https://doi.org/10.1017/cbo9780511973000
Selection of important features is very vital in machine learning tasks involving high-dimensional dataset with large features. It helps in reducing the dimensionality of a dataset and improving model performance. Most of the feature select...
10.46481/jnsps.2025.2273
https://doi.org/10.1016/0305-0548(86)90048-1
A time-series of numerical data and a sequence of time-ordered documents are often correlated. This paper aims at modeling the impact that the underlying themes discussed in the text data have on the time series. To do so, we introduce an o...
10.1109/ACCESS.2022.3202960
This work presents a comprehensive overview of nature-inspired optimization algorithms their components, classifications, and applications in various domains. The paper focuses on the importance of basic technique in swarm optimization algo...
10.33899/iqjoss.v23i1.62135
(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the m...
10.3390/ai5040118
The emergence of cooperation among self-interested agents in social dilemmas remains a central unsolved problem in evolutionary game theory, multi-agent systems, and behavioral science. While memory-one reciprocal strategies such as Tit-for...
10.3389/frai.2026.1844089
Diabetic retinopathy (DR) is a common complication of diabetes that damages retinal blood vessels and can lead to vision impairment. The application of Artificial Intelligence (AI) in DR screening offers a promising alternative to conventio...
10.15416/pcpr.v9i3.60068
Abstract —Large-scale engineering optimization problems, characterized by hundreds or thousands of decision variables, complex constraints, and highly nonlinear objective functions, present significant challenges for conventional optimizati...
10.1007/s44196-026-01291-2
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machi...
https://doi.org/10.1201/b22400
https://doi.org/10.1007/978-3-030-26622-6
being transfixed by the 1972 interminable match between challenger Bobby Fischer and artificial intelligence: Past and future...
https://doi.org/10.1145/2063176.2063177
The tendency of generative artificial intelligence (AI) to ‘hallucinate’ false information is well known; AI-generated citations to non-existent sources have penetrated the bibliographies of peer-reviewed publications. D...
10.3897/ese.2025.e153973