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Physical Classification

The data set was contributed to the benchmark collection by Terry Sejnowski, now at the Salk Institute and the Universit......

数据结构 ? 62K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    The data set was contributed to the benchmark collection by Terry Sejnowski, now at the Salk Institute and the University of California at San Deigo.  The data set was developed in collaboration with R. Paul Gorman of Allied-Signal Aerospace Technology Center.

    Data Set Information:

    The file "sonar.mines" contains 111 patterns obtained by bouncing sonar signals off a metal cylinder at various angles and under various conditions.  The file "" contains 97 patterns obtained from rocks under similar conditions.  The transmitted sonar signal is a frequency-modulated chirp, rising in frequency.  The data set contains signals obtained from a variety of different aspect angles, spanning 90 degrees for the cylinder and 180 degrees for the rock.

    Each pattern is a set of 60 numbers in the range 0.0 to 1.0.  Each number represents the energy within a particular frequency band, integrated over a certain period of time.  The integration aperture for higher frequencies occur later in time, since these frequencies are transmitted later during the chirp.

    The label associated with each record contains the letter "R" if the object is a rock and "M" if it is a mine (metal cylinder).  The numbers in the labels are in increasing order of aspect angle, but they do not encode the angle directly.

    Attribute Information:


    Relevant Papers:

    1. Gorman, R. P., and Sejnowski, T. J. (1988).  "Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets" in Neural Networks, Vol. 1, pp. 75-89.
    [Web link]

    Papers That Cite This Data Set1:

    Zhi-Hua Zhou and Yuan Jiang. NeC4.5: Neural Ensemble based C4.5. IEEE Trans. Knowl. Data Eng, 16. 2004.  [View Context].

    Jianbin Tan and David L. Dowe. MML Inference of Oblique Decision Trees. Australian Conference on Artificial Intelligence. 2004.  [View Context].

    Jeremy Kubica and Andrew Moore. Probabilistic Noise Identification and Data Cleaning. ICDM. 2003.  [View Context].

    Dennis DeCoste. Anytime Query-Tuned Kernel Machines via Cholesky Factorization. SDM. 2003.  [View Context].

    Ayhan Demiriz and Kristin P. Bennett and Mark J. Embrechts. A Genetic Algorithm Approach for Semi-Supervised Clustering. E-Business Department, Verizon Inc.. 2002.  [View Context].

    Michail Vlachos and Carlotta Domeniconi and Dimitrios Gunopulos and George Kollios and Nick Koudas. Non-linear dimensionality reduction techniques for classification and visualization. KDD. 2002.  [View Context].

    Xavier Llor and David E. Goldberg and Ivan Traus and Ester Bernad i Mansilla. Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection. IWLCS. 2002.  [View Context].

    Fei Sha and Lawrence K. Saul and Daniel D. Lee. Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS. 2002.  [View Context].

    Marina Skurichina and Ludmila Kuncheva and Robert P W Duin. Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy. Multiple Classifier Systems. 2002.  [View Context].

    Dennis DeCoste. Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry. ICML. 2002.  [View Context].

    Wl/odzisl/aw Duch and Karol Grudzinski. Ensembles of Similarity-based Models. Intelligent Information Systems. 2001.  [View Context].

    Juan J. Rodr##guez and Carlos J. Alonso and Henrik Bostrom. Boosting Interval based Literals. 2000.  [View Context].

    Chris Drummond and Robert C. Holte. Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria. ICML. 2000.  [View Context].

    Carlotta Domeniconi and Jing Peng and Dimitrios Gunopulos. An Adaptive Metric Machine for Pattern Classification. NIPS. 2000.  [View Context].

    Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Improved Generalization Through Explicit Optimization of Margins. Machine Learning, 38. 2000.  [View Context].

    Kristin P. Bennett and Ayhan Demiriz and John Shawe-Taylor. A Column Generation Algorithm For Boosting. ICML. 2000.  [View Context].

    Chris Drummond and Robert C. Holte. Explicitly representing expected cost: an alternative to ROC representation. KDD. 2000.  [View Context].

    Stavros J. Perantonis and Vassilis Virvilis. Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis. Neural Processing Letters




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