Under the Internet information network environment, College English teaching mode is faced with a new choice and transformation. SPOC and STEAM classes have multiple advantages, which can make up for the lack of a single teaching model and provide new ideas for teaching reform. It is a practical application of statistical method to optimize the model of artificial neural network by machine learning method of statistics. The application of mathematical statistics can solve some related problems of artificial perception. Therefore, artificial neural network has the same simple decision ability and judgment ability as human beings. In this paper, the authors analyze the image invariant features and SVM algorithms application in college English education platform. The results show that this method has a positive effect on learners’ English proficiency and learning effect. Teachers also avoid paying a lot of labor, which is very beneficial to the implementation of innovative teaching. However, compared with the traditional teaching, the phenomenon of student achievement differentiation is very serious, and teaching is facing great pressure. Therefore, improving students’ autonomous learning ability and teachers’ information literacy is still very helpful to improve the teaching effect.
das Chagas MouraM., ZioE., 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.
2.
HuS., YaoY.D. and YangZ., MAC protocol identification using support vector machines for cognitive radio networks, IEEE Wireless Communications21(1) (2014), 52–60.
3.
KangM., KimJ., 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.
4.
AtienzaF.A., MorgadoE., FernandezmartinezL., et al., Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines, IEEE Trans Biomed Eng61(3) (2014), 832–840.
5.
MushtaqM., AkramU., KhanI. and KhanS., Cloud computing environment and security challenges: A review, International Journal of Advanced Computer Science and Applications8(10) (2017), 183–195.
6.
ZhangR., GaoM., HeX. and ZhouA., Learning user credibility for product ranking, Knowledge and Information Systems46 (2016), 679–705.
7.
WangX. and SongY., Uncertainty measure in evidence theory with its applications, Applied Intelligence48(7) (2018), 1672–1688.
8.
BleischS., DuckhamM., GaltonA. and LaubeP., Mining candidate causal relationships in movement patterns, International Journal of Geographical Information Science28(2) (2014), 363–382.
9.
MishraD., SamantarayS. and JoosG., A combined wavelet and data-mining based intelligent protection scheme for microgrid, IEEE Trans Smart Grid7(5) (2016), 2295–2304.
10.
BettinsoliM., MaassA., KashimaY. and SuitnerC., Word-order and causal inference: The temporal attribution bias, Journal of Experimental Social Psychology60 (2015), 144–149.
11.
JilaniA., CramptonD. and KitchinM.V., Ascol: A tool for improving automatic planning domain model acquisition, Congress of the Italian Association for Artificial Intelligence Springer, (2015), 438–451.
12.
NatarajanS., BangeraV., KhotT., PicadoJ. and WazalwarA., Markov logic networks for adverse drug event extraction from text, Knowledge and Information Systems51(2) (2017), 435–457.
13.
ParkinsonS., SomarakiV. and WardR., Auditing file system permissions using association rule mining, Expert Systems with Applications55 (2016), 274–283.
14.
TreurJ., Dynamic modeling based on a temporal–causal network modeling approach, Biologically Inspired Cognitive Architectures16 (2016), 131–168.
15.
WangL., MengJ. and XuP., Mining temporal association rules with frequent itemsets tree, Applied Soft Computing62 (2018), 817–829.
16.
MishraD., SamantarayS. and JoosG., A combined wavelet and data-mining based intelligent protection scheme for microgrid, IEEE Trans Smart Grid7(5) (2016), 2295–2304.
17.
BhattU. and PatelP., A novel approach for finding rare items based on multiple minimum support framework, Procedia Computer Science57 (2015), 1088–1095.
18.
JiY., YingH., TranJ. and DewsP., A method for mining infrequent causal associations and its application in finding adverse drug reaction signal pairs, IEEE Transactions on Knowledge and Data Engineering25(4) (2013), 721–733.
19.
KohH. and TanG., Data mining applications in healthcare, Journal of Healthcare Information Management19(2) (2011), 65–69.
20.
NaharJ., ImamT. and TickleK., Association rule mining to detect factors which contribute to heart disease in males and females, Expert Systems with Applications40(4) (2013), 1086–1093.
21.
YunU. and LeeG., Incremental mining of weighted maximal frequent itemsets from dynamic databases, Expert Systems with Applications54 (2016), 304–327.
22.
SohailS. and SiddiquiJ., Feature extraction and analysis of online reviews for the recommendation of books using opinion mining technique, Perspectives in Science8 (2016), 754–756.
23.
SavageD., ZhangX. and YuX., Detection of opinion spam based on anomalous rating deviation, Expert Systems with Applications42 (2015), 8650–8657.
24.
McIlroyS., AliN. and KhalidH., Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews, Empirical Software Engineering21 (2016), 1067–1106.
25.
YinJ. and ShiY., Some Equivalent Conditions for Dynamical Systems To Be Transitive and Sensitive with Respect to Furstenberg Families, Dynamic Systems and Applications28 (2019), 545–559.
26.
BaoH., New p-th Moment Exponential Stability Criteria for FCNNs with Time-Varying Delays and Stochastic Effects, Dynamic Systems and Applications29(2) (2020), 355–366.
27.
PaulA., JiangY.C., WangJ.F. and YangJ.F., Parallel reconfigurable computing-based mapping algorithm for motion estimation in advanced video coding, ACM Transactions on Embedded Computing Systems (TECS)11(S2) (2012), 1–18.
28.
RathoreM.M., PaulA., HongW.H., SeoH.C., AwanI. and SaeedS., Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data, Sustainable Cities and Society40 (2018), 600–610.
29.
PaulA., Internet of Things: A primer’, R Jeyaraj Human Behavior and Emerging Technologies1(1) (2019), 37–47.
30.
PaulA., DanielA., AhmadA. and RhoS., Cooperative cognitive intelligence for internet of vehicles, IEEE Systems Journal11(3) (2017), 1249–1258.
31.
PaulA., AhmadA., Mazhar RathoreM. and JabbarS., Smartbuddy: defining human behaviors using big data analytics in social internet of things, IEEE Wireless Communications23(5) (2016), 68–74.
