MLife: a lite framework for machine learning lifecycle initialization

Machine Learning - Machine learning (ML) lifecycle is a cyclic process to build an efficient ML system. Though a lot of commercial and community (non-commercial) frameworks have been proposed to...     

基于SVM多分类决策树的研究综述

支持向量机(Support Vector Machine,SVM)是一种基于统计学习理论的机器学习方法,由于其出色的学习性能,早已成为当前机器学习界的研究热点;而决策树是一种功能强大且相当受欢迎的分类和预测工具。本文重点介绍支持向量机与决策树结合解决多分类问题的算法,并对其进行评析和总结。     

面向人体动作识别的随机增量型混合学习机模型

针对自然人机交互应用中的人体动作识别问题,总结了传统机器学习模型在识别人体动作时的缺点,然后在此基础上针对自然人机交互应用的独特要求提出了面向人体动作识别的随机增量型混合学习机模型.该模型将误差反向传播模型、增量型极限学习机模型和双端增量型极限学习机模型相结合,克服了传统方法在识别人体动作时的不足.详细阐述了针对面向人体动作识别的随机增量型混合学习机模型的算法理论、模型合理性和实现方案.最后通过对比识别实验结果,验证了随机增量型混合学习机模型在识别人体动作问题上具有更好的鲁棒性、实时性和准确性.     

Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation

Machine Learning - We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain...     

Combination of inductive mondrian conformal predictors

Machine Learning - It is well known that ensembling predictions from different Machine Learning (ML) algorithms can improve accuracy. This paper proposes a approach to combine Conformal Predictors...     

Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review

Multimedia Tools and Applications - As everyone knows that in today’s time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are...     

自适应混沌粒子群算法对极限学习机参数的优化

针对极限学习机（ELM）在处理非线性数据时效果不理想,并且ELM的参数随机化不利于模型泛化的特点,提出了一种改进的极限学习机算法。结合自适应混沌粒子群（ACPSO）算法对ELM的参数进行优化,以增强算法的稳定性,提高ELM对基因表达数据分类的精度。在UCI基因数据集上进行仿真实验,实验结果表明,与探测粒子群-极限学习机（DPSO-ELM）、粒子群-极限学习机（PSO-ELM）等算法相比,自适应混沌粒子群-极限学习机（ACPSO-ELM）算法具有较好的稳定性、可靠性,且能有效提高基因分类精度。     

Lessons on off-policy methods from a notification component of a chatbot

Machine Learning - This work serves as a review of our experience applying off-policy techniques to train and evaluate a contextual bandit model powering a troubleshooting notification in a...     

Boosting as a kernel-based method

Machine Learning - Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and...     

Toward optimal probabilistic active learning using a Bayesian approach

Machine Learning - Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling...     

HIVE-COTE 2.0: a new meta ensemble for time series classification

Machine Learning - The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from...     

基于ELM-LSSVM的网络流量预测

为了对网络流量进行准确预测,针对传统极限学习机的“过拟合”不足,提出一种极限学习机和最小二乘支持向量机相融合的网络流量预测模型（ELM-LSSVM）。该模型通过相空间重构获得网络流量的学习样本,引入最小二乘支持向量机对极限学习进行改进,并对网络流量训练集进行学习,采用仿真实验对模型性能进行测试。结果表明,ELM-LSSVM提高了网络流量的预测精度,实现了网络流量准确预测,并具有较强的实际应用价值。     

基于遗传算法改进诗词风格判别的研究

本文对诗词采用向量空间模型来表示,基于机器学习中的朴素贝叶斯等方法,首次提出了古典诗词的豪放和婉约风格判别计算模型,并用遗传算法对模型进行改进,取得较好的诗词风格判别结果。该模型已经在精典诗词语料的机器学习基础上得以实现,并且获得较好的诗词风格判别效果。     

What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation

Machine Learning - What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increasingly important as people have tried to apply the GA to ever more diverse types of...     

Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach

Machine Learning - Considering the data collection and labeling cost in real-world applications, training a model with limited examples is an essential problem in machine learning, visual...     

Machine learning in information systems - a bibliographic review and open research issues

Electronic Markets - Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant...     

Re-thinking model robustness from stability: a new insight to defend adversarial examples

Machine Learning - We study the model robustness against adversarial examples, referred to as small perturbed input data that may however fool many state-of-the-art deep learning models. Unlike...     

Representing the behaviour of supervised classification learning algorithms by Bayesian networks

In this paper, an approach to study the nature of the classification models induced by Machine Learning algorithms is proposed. Instead of the predictive accuracy, the values of the predicted class labels are used to characterize the classification models. Over these predicted class labels Bayesian networks are induced. Using these Bayesian networks, several assertions are extracted about the nature of the classification models induced by Machine Learning algorithms.     

Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series

Machine Learning - Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time...