With the popularization of information and the establishment of the databases in great number, and how to extract data from the useful information is the urgent problem to be solved. Machine learning is the core issue of artificial intelligence research, this paper introduces the definition of machine learning and its basic structure, and describes a variety of machine learning methods, including rote learning, inductive learning, analogy learning , explained learning, learning based on neural network and knowledge discovery and so on. This paper also brings foreword the objectives of machine learning, and points out the development trend of machine learning.
Abstract-Loop closure detection is an essential component for simultaneously localization and mapping in a variety of robotics applications. One of the most challenging problems is to perform long-term place recognition with strong perceptual aliasing and appearance variations due to changes of illumination, vegetation, weather, etc. To address this challenge, we propose a novel Robust Multimodal Sequence-based (ROMS) method for long-term loop closure detection, by formulating image sequence matching as an optimization problem regularized by structured sparsity-inducing norms. Our method is able to model the sparsity nature of place recognition, i.e., the current location should match only a small subset of previously visited places, as well as to model underlying structures of image sequences and incorporate multiple feature modalities to construct a discriminative scene representation. In addition, a new optimization algorithm is developed to efficiently solve the formulated problem, which has a theoretical guarantee to converge to the global optimal solution. To evaluate the ROMS algorithm, extensive experiments are performed using large-scale benchmark datasets, including St Lucia, CMU-VL, and Nordland datasets. Experimental results have validated that our algorithm outperforms previous loop closure detection methods, and obtains the state-of-the-art performance on long-term place recognition.
AIM: To compare clinical outcomes and refractive stability of implantable collamer lens (ICL) implantation and femtosecond laser assisted laser in situ keratomileusis (FS-LASIK) for high myopia correction. METHODS: The Optical Quality Analysis System (OQAS) was used to evaluate clinical outcomes objectively after operation for high myopia correction. We compared the two procedures in terms of 1-year changes in uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), safety index, efficacy index, spherical equivalent, modulation transfer function (MTF) cutoff frequency, strehl ratio (SR) and objective scatter index (OSI). RESULTS: At 1y postoperatively, the safety indices were 1.33±0.27 in ICL group, and 1.17±0.24 in FS-LASIK group. 39.58% in the ICL group and 27.59% in the FS-LASIK group gained CDVA in 2 lines or better than that in preoperative CDVA. The efficacy indices were 1.28±0.22 in ICL group, and 1.13±0.26 in FS-LASIK group. The changes of spherical equivalent from 1wk to 1y postoperatively was -0.12±0.37 D in ICL group, and -0.79±0.58 D in FS-LASIK group (P<0.05). Spherical equivalent within ±0.50 D was achieved in 97.92% in ICL group and 68.97% in FS-LASIK group. MTF cutoff frequency were higher with ICL as compared to FS-LASIK (P<0.05) at each postoperative follow-up stage; for postoperative 1mo later, SR was statistically significant difference between two groups (P<0.05); with no statistically significant difference in OSI between two groups (P>0.05) in postoperative 3mo later. CONCLUSION: ICL implantation and FS-LASIK procedures both provide good safety and predictability in high myopia correction. ICL implantation provides better clinical outcomes and refractive stability than FS-LASIK.
This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, threestream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce imageclassified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89%, with a receiver operating characteristic of 93%. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately.
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