79,659 research outputs found
Learning Vector Quantization:Generalization ability and dynamics of competing prototypes
Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way, aiming at understanding the influence of the number of prototypes and their assignment to classes. The theory of on-line learning allows a mathematical description of the learning dynamics in model situations. We demonstrate using a system of three prototypes the different behaviors of LVQ systems of multiple prototype and single prototype class representation.
marl-jax: Multi-Agent Reinforcement Leaning Framework
Recent advances in Reinforcement Learning (RL) have led to many exciting
applications. These advancements have been driven by improvements in both
algorithms and engineering, which have resulted in faster training of RL
agents. We present marl-jax, a multi-agent reinforcement learning software
package for training and evaluating social generalization of the agents. The
package is designed for training a population of agents in multi-agent
environments and evaluating their ability to generalize to diverse background
agents. It is built on top of DeepMind's JAX ecosystem~\cite{deepmind2020jax}
and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is
capable of working in cooperative and competitive, simultaneous-acting
environments with multiple agents. The package offers an intuitive and
user-friendly command-line interface for training a population and evaluating
its generalization capabilities. In conclusion, marl-jax provides a valuable
resource for researchers interested in exploring social generalization in the
context of MARL. The open-source code for marl-jax is available at:
\href{https://github.com/kinalmehta/marl-jax}{https://github.com/kinalmehta/marl-jax}Comment: Accepted at ECML-PKDD 2023 Demo Trac
On-Line AdaTron Learning of Unlearnable Rules
We study the on-line AdaTron learning of linearly non-separable rules by a
simple perceptron. Training examples are provided by a perceptron with a
non-monotonic transfer function which reduces to the usual monotonic relation
in a certain limit. We find that, although the on-line AdaTron learning is a
powerful algorithm for the learnable rule, it does not give the best possible
generalization error for unlearnable problems. Optimization of the learning
rate is shown to greatly improve the performance of the AdaTron algorithm,
leading to the best possible generalization error for a wide range of the
parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
- …