Zobrazeno 1 - 10
of 403
pro vyhledávání: '"Tsetlin machine"'
Tsetlin Machines (TMs) have emerged as a compelling alternative to conventional deep learning methods, offering notable advantages such as smaller memory footprint, faster inference, fault-tolerant properties, and interpretability. Although various a
Externí odkaz:
http://arxiv.org/abs/2410.17851
Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely on deep learning algorithms. Thes
Externí odkaz:
http://arxiv.org/abs/2409.03544
The Tsetlin Machine (TM) has achieved competitive results on several image classification benchmarks, including MNIST, K-MNIST, F-MNIST, and CIFAR-2. However, color image classification is arguably still in its infancy for TMs, with CIFAR-10 being a
Externí odkaz:
http://arxiv.org/abs/2406.00704
This paper introduces the Sparse Tsetlin Machine (STM), a novel Tsetlin Machine (TM) that processes sparse data efficiently. Traditionally, the TM does not consider data characteristics such as sparsity, commonly seen in NLP applications and other ba
Externí odkaz:
http://arxiv.org/abs/2405.02375
Green Tsetlin (GT) is a Tsetlin Machine (TM) framework developed to solve real-world problems using TMs. Several frameworks already exist that provide access to TM implementations. However, these either lack features or have a research-first focus. G
Externí odkaz:
http://arxiv.org/abs/2405.04212
System-on-Chip Field-Programmable Gate Arrays (SoC-FPGAs) offer significant throughput gains for machine learning (ML) edge inference applications via the design of co-processor accelerator systems. However, the design effort for training and transla
Externí odkaz:
http://arxiv.org/abs/2403.10538
Autor:
Morris, Jordan
This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. Th
Externí odkaz:
http://arxiv.org/abs/2403.09680
We propose a novel way of assessing and fusing noisy dynamic data using a Tsetlin Machine. Our approach consists in monitoring how explanations in form of logical clauses that a TM learns changes with possible noise in dynamic data. This way TM can r
Externí odkaz:
http://arxiv.org/abs/2310.17207
Autor:
Bhattarai, Bimal, Granmo, Ole-Christoffer, Jiao, Lei, Andersen, Per-Arne, Tunheim, Svein Anders, Shafik, Rishad, Yakovlev, Alex
In this paper, we introduce a sparse Tsetlin Machine (TM) with absorbing Tsetlin Automata (TA) states. In brief, the TA of each clause literal has both an absorbing Exclude- and an absorbing Include state, making the learning scheme absorbing instead
Externí odkaz:
http://arxiv.org/abs/2310.11481
Autor:
Granmo, Ole-Christoffer, Andersen, Per-Arne, Jiao, Lei, Zhang, Xuan, Blakely, Christian, Tveit, Tor
A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable. If the blanket cannot be reduced without losing useful information, it is called a Markov boundary. Identifying the M
Externí odkaz:
http://arxiv.org/abs/2309.06315