Tag: Classification

Classification Algorithms for Codes and Designs


Free Download Classification Algorithms for Codes and Designs by Petteri Kaski , Patric R.J. Östergård
English | PDF | 2006 | 414 Pages | ISBN : 3540289909 | 4 MB
A new starting-point and a new method are requisite, to insure a complete [classi?cation of the Steiner triple systems of order 15]. This method was furnished, and its tedious and di?cult execution und- taken, by Mr. Cole. F. N. Cole, L. D. Cummings, and H. S. White (1917) [129] The history of classifying combinatorial objects is as old as the history of the objects themselves. In the mid-19th century, Kirkman, Steiner, and others became the fathers of modern combinatorics, and their work – on various objects, including (what became later known as) Steiner triple systems – led to several classi?cation results. Almost a century earlier, in 1782, Euler [180] published some results on classifying small Latin squares, but for the ?rst few steps in this direction one should actually go at least as far back as ancient Greece and the proof that there are exactly ?ve Platonic solids. One of the most remarkable achievements in the early, pre-computer era is the classi?cation of the Steiner triple systems of order 15, quoted above. An onerous task that, today, no sensible person would attempt by hand calcu- tion. Because, with the exception of occasional parameters for which com- natorial arguments are e?ective (often to prove nonexistence or uniqueness), classi?cation in general is about algorithms and computation.

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An Introduction to Image Classification


Free Download An Introduction to Image Classification: From Designed Models to End-to-End Learning
English | 2024 | ISBN: 9819978815 | 481 Pages | PDF EPUB (True) | 71 MB
Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with deep neural networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight.

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Classification and Data Science in the Digital Age


Free Download Classification and Data Science in the Digital Age by Paula Brito, José G. Dias, Berthold Lausen, Angela Montanari, Rebecca Nugent
English | PDF (True) | 2023 | 393 Pages | ISBN : 3031090330 | 23.9 MB
The contributions gathered in this book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education.

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Progress on Pattern Classification, Image Processing and Communications


Free Download Progress on Pattern Classification, Image Processing and Communications
English | 2023 | ISBN: 3031416295 | 391 Pages | PDF EPUB (True) | 46 MB
This book presents a collection of high-quality research papers accepted to multi-conference consisting of the 13th International Conference on Image Processing and Communications (IP&C 2023), the 13th International Conference on Computer Recognition Systems (CORES 2023) held jointly in Wroclaw, Poland (virtually), in June 2023.

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TensorFlow Developer Certificate – Image Classification


Free Download TensorFlow Developer Certificate – Image Classification
Released 11/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + vtt | Duration: 2h 34m | Size:
As part of the TensorFlow Developer certification, this course focuses on computer vision. By the end of the course, you will know everything to build computer vision neural networks that can handle complex real-world images using TensorFlow.

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