2023年度 人工知能学会全国大会（第37回）
2023年6月6日〜6月9日熊本城ホール(熊本県熊本市) ＋ オンライン
2023年度 人工知能学会全国大会（第37回）
2023年6月6日〜6月9日熊本城ホール(熊本県熊本市) ＋ オンライン
[2U4-IS-2c-01]Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learningWe show how using staple techniques such as NN or GPR as building blocks of a more involved method can enhance ML capabilities in high dimension and or with sparse data
〇Sergei Manzhos1, Shunsaku Tsuda1, Hyojae Lee1, Manabu Ihara1(1. Tokyo Institute of Technology)
[[Online, Regular]]
Machine learning (ML) techniques such as neural networks (NN) and Gaussian process regressions (GPR) are now widely used in diverse applications. While each technique has pros and cons, they are all challenged when faced with high dimensionality of the feature space or low and uneven data density. We will demonstrate how combining them with high-dimensional model representations (HDMR) results in methods better apt to deal with these issues. HDMR-NN, HDMR-GPR combinations and NN with HDMR-GPR neuron activation functions will be presented with examples ranging from computational chemistry to quantitative finance.