Technological innovation in manufacturing industry is a kind of R&D activity that produces new technologies, including input and output of technological innovation. In this paper, the authors analyze the lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology. Data mining can discover novel, effective, potential and ultimately understandable data patterns from a deeper level, and encode the data to predict the development trend of enterprises. The machine learning support vector machine method is used to analyze and model the collected data. At the same time, we constructed a decision tree using random forest, and explained the significance of the training algorithm through the visualization results. The simulation results show that learning growth dimension and market dimension have the greatest impact on business model innovation. In the context of TEC, business model innovation must pay attention to market grasp and customer demand oriented, so as to improve the competitiveness of manufacturing enterprises.
ShiJ., LeeW.J. and LiuY.O., et al., Forecasting power output of photovoltaic system based on weather classification and support vector machine, IEEE Transactions on Industry Applications48(3) (2015), 1064–1069.
2.
TianY., QiZ. and JuX., et al., Nonparallel support vector machines for pattern classification, IEEE Transactions on Cybernetics44(7) (2013), 1067.
3.
KavzogluT., SahinE.K. and ColkesenI., Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression, Landslides11(3) (2014), 425–439.
4.
KremerJ., Steenstrup PedersenK. and IgelC., Active learning with support vector machines, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery4(4) (2014), 313–326.
5.
AzarA.T. and El-SaidS.A., Performance analysis of support vector machines classifiers in breast cancer mammography recognition, Neural Computing & Applications24(5) (2014), 1163–1177.
6.
HuS., YaoY.D. and YangZ., MAC protocol identification using support vector machines for cognitive radio networks, IEEE Wireless Communications21(1) (2014), 52–60.
7.
dasM., ChagasMoura, ZioE. and LinsI.D., et al., Failure and reliability prediction by support vector machines regression of time series data, Reliability Engineering & System Safety96(11) (2017), 1527–1534.
8.
BenitezJ., MiyazakiS., YanagawaY. and OkuboH., Application of six sigma methodology to reduce waiting times at okayama university hospital, Produetion Management2008(2).
9.
LuoD. and WangJ., Performance evaluation of collaborative innovation center in universities of henan province based on two-dimensional matrix, Journal of North China University of Water Resources & Electric Power (2016), 42–50.
10.
SandersR. Nada, Pattern of information technology use: The impact on buyer– suppler coordination and performance, Journal of Operations Management(3) (2007), 115–120.
11.
TaoQ., WeiH. and TaoS., Evaluation on efficiency of independent innovation of TCM industry based on DEA model-an example of anhui province, Science & Technology Management Research (2016).
12.
KangM., KimJ. and KimJ.M., et al., Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis, IEEE Transactions on Power Electronics30(5) (2015), 2786–2797.
13.
AtienzaF.A., MorgadoE. and FernandezmartinezL., et al., Detection of life-threatening arrhythmias using feature selection and support vector machines, IEEE Trans Biomed Eng61(3) (2014), 832–840.
14.
YongD., BhowmikS. and MagnagoF., An effective power quality classifier using wavelet transform and support vector machines, Expert Systems with Applications42(15-16) (2015), 6075–6081.
15.
ClaesenM., De SmetF. and SuykensJ.A.K., et al., EnsembleSVM: A library for ensemble learning using support vector machines, Journal of Machine Learning Research15(1) (2014), 141–145.
16.
GaoL., LiJ. and KhodadadzadehM., et al., Subspace-based support vector machines for hyperspectral image classification, IEEE Geoscience and Remote Sensing Letters12(2) (2015), 349–353.
17.
ZhangS., YuX. and SuiY., et al., Object tracking with multi-view support vector machines, IEEE Transactions on Multimedia17(3) (2015), 1–1.
18.
IfenthalerD. and WidanapathiranaC., Development and validation of a learning analytics framework: Two case studies using support vector machines, technology, Knowledge and Learning19(1-2) (2014), 221–240.
19.
JenkinsonT. and MayerC., The assessment: Corporate governance and corporate control, Oxford Review of Economic Policy8 (2012), 1–10.
20.
KaplanR., Using the balanced scorecard as a strategic management system, Harvard Business Review74 (1996), 75–85.
21.
NiniG. and SmithC., Creditor control rights, corporate governance, and firm value, Review of Financial Studies25 (2012), 1713–1761.
22.
ShleiferA. and VishnyR., A survey of corporate governance, The Journal of Finance52 (1997), 737–783.
23.
CaoR., WangZ. and WangY., et al., SMOQ: A tool for predicting the absolute residue-specific quality of a single protein model with support vector machines, BMC Bioinformatics (2014), 15–21.
24.
PengJ., ZhouY. and ChenC.L.P., Region-kernel-based support vector machines for hyperspectral image classification, IEEE Transactions on Geoscience & Remote Sensing53(9) (2015), 4810–4824.
25.
LinT.C. and YuP.T., Adaptive two-pass median filter based on support vector machines for image restoration, Neural Computation16(2) (2004), 333–354.
26.
MajidA., AliS. and IqbalM., et al., Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines, Computer Methods & Programs in Biomedicine113(3) (2014), 792–808.
