Machine Learning: A Review
Keywords:
Algorithm, Bioinformatics, RetrievalAbstract
In today’s scenario, big number of data is generated` everywhere.
Consequently, using these data we can make valuable information and
find useful data we generate a sufficient algorithm. The algorithm which
we wrote is called data mining or machine learning. Machine learning
is an intensity subpart of AI, and AI is used to create the algorithm by
using previous data. In today’s world, Machine learning is used in many
fields such as bioinformatics, intrusion detection, Information retrieval,
game playing, marketing, malware detection, image convolution and
so on. This paper gives info about the component of machine learning.
References
Linden G, Smith B, York J. Amazon.com Recommen
dations Item-to-Item Collaborative Filtering. IEEE
Computer Society 2003.
Wang J, de Vries AP, Reinders MJT. Unifying Userbased
and Itembased Collaborative Filtering Approaches by
Similarity Fusion. 2006 ACM.
Symeonidis P, Nanopoulos A, Papadopoulos AN et
al. Collaborative recommender systems: Combining
effectiveness and efficiency. 2007 Elsevier Ltd.
Yoshii K, Goto M, Komatani K et al. An Efficient Hybrid
Music Recommender System Using an Incrementally
TrainableProbabilistic Generative Model. IEEE 2008
Umyarov A, Tuzhilin A. Improving Collaborative Filtering
Recommendations Using External Data. 2008 IEEE.
Cheng Z, Hurley N. Effective Diverse and Obfuscated
Attacks on Model-based Recommender Systems. 2009
ACM.
Kwon HJ, Lee TH, Hong KS. Improved Memory-based
Collaborative Filtering Using Entropy-based Similarity
Measures, 2009 Academy Publisher.
Gunawardana A, Meek C. A Unified Approach to
Building Hybrid Recommender Systems. 200X ACM
Cleger-Tamayo S, Fern´andez-Luna JM, Huete JF. A
New Criteria for Selecting Neighborhood in MemoryBased Recommender Systems. Springer-Verlag Berlin
Heidelberg 2011.
Lops P, de Gemmis M, Semeraro G. Content-based
Recommender Systems: State of the Art and Trends.
Springer 2011
