Exception in thread "main" java.lang.NoClassDefFoundError: classpath

I am trying to run Wikipedia Bayes Example from
https://cwiki.apache.org/confluence/...+Bayes+Example

When I ran the following command : $MAHOUT_HOME/bin/mahout
wikipediaXMLSplitter -d
$MAHOUT_HOME/examples/temp/enwiki-latest-pages-articles10.xml -o
wikipedia/chunks -c 64

I am getting this error:

Exception in thread "main" java.lang.NoClassDefFoundError: classpath
Caused by: java.lang.ClassNotFoundException: classpath
        at java.net.URLClassLoader$1.run(URLClassLoader.java:217)
        at java.security.AccessController.doPrivileged(Native Method)
        at java.net.URLClassLoader.findClass(URLClassLoader.java:205)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:323)
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:294)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:268)
        at java.lang.ClassLoader.loadClassInternal(ClassLoader.java:336)
Running on hadoop, using /x/home/hadoop_adm/opt/hadoop/bin/hadoop and
HADOOP_CONF_DIR=
MAHOUT-JOB: /x/home/user/mahout-distribution-0.7/mahout-examples-0.7-job.jar
12/07/27 16:28:02 WARN driver.MahoutDriver: Unable to add class:
wikipediaXMLSplitter
12/07/27 16:28:02 WARN driver.MahoutDriver: No wikipediaXMLSplitter.props
found on classpath, will use command-line arguments only
Unknown program 'wikipediaXMLSplitter' chosen.
Valid program names are:
  arff.vector: : Generate Vectors from an ARFF file or directory
  baumwelch: : Baum-Welch algorithm for unsupervised HMM training
  canopy: : Canopy clustering
  cat: : Print a file or resource as the logistic regression models would
see it
  cleansvd: : Cleanup and verification of SVD output
  clusterdump: : Dump cluster output to text
  clusterpp: : Groups Clustering Output In Clusters
  cmdump: : Dump confusion matrix in HTML or text formats
  cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
  cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
  dirichlet: : Dirichlet Clustering
  eigencuts: : Eigencuts spectral clustering
  evaluateFactorization: : compute RMSE and MAE of a rating matrix
factorization against probes
  fkmeans: : Fuzzy K-means clustering
  fpg: : Frequent Pattern Growth
  hmmpredict: : Generate random sequence of observations by given HMM
  itemsimilarity: : Compute the item-item-similarities for item-based
collaborative filtering
  kmeans: : K-means clustering
  lucene.vector: : Generate Vectors from a Lucene index
  matrixdump: : Dump matrix in CSV format
  matrixmult: : Take the product of two matrices
  meanshift: : Mean Shift clustering
  minhash: : Run Minhash clustering
  parallelALS: : ALS-WR factorization of a rating matrix
  recommendfactorized: : Compute recommendations using the factorization of
a rating matrix
  recommenditembased: : Compute recommendations using item-based
collaborative filtering
  regexconverter: : Convert text files on a per line basis based on regular
expressions
  rowid: : Map SequenceFile<Text,VectorWritable> to
{SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}
  rowsimilarity: : Compute the pairwise similarities of the rows of a matrix
  runAdaptiveLogistic: : Score new production data using a probably trained
and validated AdaptivelogisticRegression model
  runlogistic: : Run a logistic regression model against CSV data
  seq2encoded: : Encoded Sparse Vector generation from Text sequence files
  seq2sparse: : Sparse Vector generation from Text sequence files
  seqdirectory: : Generate sequence files (of Text) from a directory
  seqdumper: : Generic Sequence File dumper
  seqmailarchives: : Creates SequenceFile from a directory containing
gzipped mail archives
  seqwiki: : Wikipedia xml dump to sequence file
  spectralkmeans: : Spectral k-means clustering
  split: : Split Input data into test and train sets
  splitDataset: : split a rating dataset into training and probe parts
  ssvd: : Stochastic SVD
  svd: : Lanczos Singular Value Decomposition
  testnb: : Test the Vector-based Bayes classifier
  trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model
  trainlogistic: : Train a logistic regression using stochastic gradient
descent
  trainnb: : Train the Vector-based Bayes classifier
  transpose: : Take the transpose of a matrix
  validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model
against hold-out data set
  vecdist: : Compute the distances between a set of Vectors (or Cluster or
Canopy, they must fit in memory) and a list of Vectors
  vectordump: : Dump vectors from a sequence file to text
  viterbi: : Viterbi decoding of hidden states from given output states
sequence

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